🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?AugustTubebuddyExpPCPAIandML&Comments& 🔥AI Engineer Masters Program (Discount Code - YTBE15): www.simplilearn.com/masters-in-artificial-intelligence?SCE-AIMasters&Comments& 🔥AI & Machine Learning Bootcamp(US Only): www.simplilearn.com/ai-machine-learning-bootcamp?NNin5Min-bfmFfD2RIcg&Comments& 🔥 Purdue Post Graduate Program In AI And Machine Learning: www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?NNin5Min-bfmFfD2RIcg&Comments&
Very informative and explained in just 5 mins - Answer is B for Quiz as the "Error is always calculated at output layer and then weight are adjusted to provide accurate results next time as application trains by itself"
Hi Shraddha, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi everyone, exactly a week ago, we conducted a quiz contest in this video. The answer to the quiz is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. We are pleased to announce the 3 lucky winners who got the right answer for our quiz: 1. Nayan Agarwal 2. Sahitya Reddy 3. Luis Mo Congratulations to all the winners! They've won an Amazon voucher worth INR 500 / $10.
Hi Jorge, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Subhadip, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
The answer is B. since the error is validated and cross-checked in the output layer after a prediction has been determined and not in every layer. I just subscribed to your channel because of this video and the blockchain one. I’m pretty sure I’ll dive deeper in to your channel since you make complex concepts seem easy. Thank you SimpliLearn!! Sending love from the Philippines!
Hi Joshua, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your constant love and support. You can dive deeper to become an AI engineer: www.simplilearn.com/artificial-intelligence-masters-program-training-course. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Very informative, this explanation is really easily understandable - Answer for the quiz question is "B" (Because error is calculated at the output layer to adjust the weight to get accurate result, not in every layer).
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Tomorrow Is my ai exam I didn't understood anything in class or by books But this video got me whole concept explain in hardly five minutes Thank you so much 😊 It saved my hours of useless attempts of my own
Hi Shweta, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Shweta, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks for the excellent explanation in a visual form. Since I teach Machine Learning, Kindly let me know how you create these animated videos. I think this may help my students to understand the concept in easy manner.
A is true as the activation function uses the threshold to determine whether is the neuron should be activated and in turn propagate data through the network. B is false as error is only calculated when the neural network makes a prediction, thus error is only calculated after the output layer. C is true as both forward and backward propagation are iterative processes during the training process. D is true as most data is processed at the hidden layers(usually one or more), most classification of the features takes place here. Answer is B
You're right! The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
Very informative and interesting video, made it really easy for me to learn neural networks. Thank you The correct answer is B- Error is always calculated at the output layer.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Hrushikesh, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hello, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Nice video sis/bro Also can a neural network can be used to find a best combination of parameters from multiple parameters Like if parameters (1,2,3,4,5,6,7,8) are fed as input , can it identify the best combination( pair of parameters) like (1&2, 1&3, 1&7, 2&8, 3&5, 7&1) for efficient performance of a system I was given this project for fuel cell performance estimation by inputing its operating and design parameters and finding the best combination which influences the performance most Can it be done in MATLAB? Pls show some light
All your videos are Awesome, you made my college exam's easier, these videos give clear cut meaning and understanding in very short time, So please do more videos
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
Answer is B. Error is calculated at each layer of the neural network. This statement is wrong since errors could only be rectified using back-propagation that occurs during the training of the neural network. Thank You Simplilearn.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Im no expert but let me try give it a shot.. initially, weights and biases are set at random..in python programming, u can use numpy.random to do this.. as your neural net process the inputs and give and output when training it, it will check how far is it from what the output supposed to be (loss value).. based on this, your neural net will adjust the weights and biases of each neurons in the hidden layer until the loss value nears zero or becomes static or the iterations assigned is completed.. depending on how well your model perform, u may have to manually changes some parameters such as number of neurons, learning rate, number of hidden layers, activation function etc.. im not sure if u can print the value of the final weight and biases of the neuron tho.. hope that helps
"Hi Faris, Here are two blogs that will help you understand how weights and biases work in a neural network: hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491 medium.com/coinmonks/the-mathematics-of-neural-network-60a112dd3e05"
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
Hi Luis, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
You are very welcome. Do show your love by subscribing our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
I don't understand what the values of x_i would be. Is it an intensity value for pixel brightness and so you're trying to detect if some pixel is bright or not? then couldn't you just investigate the edges of the image (i.e. where there's a gradient between pixels)? I don't understand.
So this is an old video so I doubt I'll get a reply, but could someone please explain why the weights vary and how the value of the weigh s are assigned? Thank you
abdullah ihsan the weight is essentially the probability each neuron will give the network that the pattern is what it is looking for and the weight is measured out of 1 So in this case if a square is input, and if neuron x1 is absolutely sure that the pixel it is looking at belongs to a square, it would give it a weighting of 1. If it is somewhat sure for example it would give it a weighting of 0.5. Over time, the better the neurons and network layers get and the more training the network gets to identify patterns correctly from input images/data, the more accurate the output results
"Hi Abdullah, Here is an article that will help you understand how weights are assigned to a neural network. towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78"
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
Great video! Two questions I have... Q1- is there an error in the formula when the weight etc is being discussed? The formula reads "(X1 * 0.8 + X3 * 0.2) + B1" should it not be (X1 * 0.8 + X2 * 0.2) + B1? and Q2 - does the neural network add each of the inputs in one formula? I.e. "(X1 * 0.8 + X3 * 0.2 + X3 * 0.1...) + B1 + B2 + B3..." and so on? Thanks!
Hi Long, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Nayan, we are sorry about that and we didn't get any response from you either. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10.
Hi Satyajit, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sana, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Hi Nikhil, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Tharun, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Tharun, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
Hi Nayan, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Sandeep, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sandeep, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi Sahitya, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below: The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi Sameed, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sameed, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Avijeet, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
Hi Manish, For a detailed understanding of how neural networks work, please check this link: th-cam.com/video/ob1yS9g-Zcs/w-d-xo.html Thank you for watching!
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Hey Jordi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
We are glad you found our video helpful. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Sorry! The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
"Hi Ahmad, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
"Hi, you got the right answer. Kudos. The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation. The correct answer to the quiz is Option B. Explanation: In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. "
![ง้อ (ALRIGHT) - FOURTH Prod. by URBOYTJ [ OFFICIAL MV ]](http://i.ytimg.com/vi/Qs8ZeV6Dqj8/mqdefault.jpg)
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?AugustTubebuddyExpPCPAIandML&Comments&
🔥AI Engineer Masters Program (Discount Code - YTBE15): www.simplilearn.com/masters-in-artificial-intelligence?SCE-AIMasters&Comments&
🔥AI & Machine Learning Bootcamp(US Only): www.simplilearn.com/ai-machine-learning-bootcamp?NNin5Min-bfmFfD2RIcg&Comments&
🔥 Purdue Post Graduate Program In AI And Machine Learning: www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?NNin5Min-bfmFfD2RIcg&Comments&
Very informative and explained in just 5 mins - Answer is B for Quiz as the "Error is always calculated at output layer and then weight are adjusted to provide accurate results next time as application trains by itself"
Hi Shraddha, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hi everyone, exactly a week ago, we conducted a quiz contest in this video. The answer to the quiz is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
We are pleased to announce the 3 lucky winners who got the right answer for our quiz:
1. Nayan Agarwal
2. Sahitya Reddy
3. Luis Mo
Congratulations to all the winners! They've won an Amazon voucher worth INR 500 / $10.
Hi Jorge, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
B
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
The Answer is :- B
Hi Subhadip, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
A 2+ hours lecture simplified in just 5 mins. This is a great resource. I will make use of it in my marching learning assignment
Hey, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
You explained it better than a course in my language which I paid for😂😂
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
why is this true😂
Use google translate
The answer is B. since the error is validated and cross-checked in the output layer after a prediction has been determined and not in every layer.
I just subscribed to your channel because of this video and the blockchain one. I’m pretty sure I’ll dive deeper in to your channel since you make complex concepts seem easy. Thank you SimpliLearn!! Sending love from the Philippines!
Hi Joshua, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your constant love and support. You can dive deeper to become an AI engineer: www.simplilearn.com/artificial-intelligence-masters-program-training-course.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Very informative, this explanation is really easily understandable - Answer for the quiz question is "B" (Because error is calculated at the output layer to adjust the weight to get accurate result, not in every layer).
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Awesome video, it explains very clearly and in a simple way how the NNs work for the beginners. Thank you!
Glad it was helpful!
Just a bunch of if/else nothing serious
"Error is calculated at each layer of the neural network" does not hold true.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Great video!!!! it was very well explained and the voice and tone is clear. THANK YOU!!!
Glad it was helpful!
Really liked the way you explained this topic.
Hey Ganesh, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Tomorrow Is my ai exam
I didn't understood anything in class or by books
But this video got me whole concept explain in hardly five minutes
Thank you so much 😊
It saved my hours of useless attempts of my own
All the best
Answer is B: Error is calculated at each layer of neural network
Hi Shweta, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Shweta, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks for the excellent explanation in a visual form. Since I teach Machine Learning, Kindly let me know how you create these animated videos. I think this may help my students to understand the concept in easy manner.
This is a scribe video. You can make use of this software to create the videos www.videoscribe.co/en"
you deserve many millions of followers, short and sweet explanation well enough to understand the concept
Thank you for your kind word and for watching!
A is true as the activation function uses the threshold to determine whether is the neuron should be activated and in turn propagate data through the network. B is false as error is only calculated when the neural network makes a prediction, thus error is only calculated after the output layer. C is true as both forward and backward propagation are iterative processes during the training process. D is true as most data is processed at the hidden layers(usually one or more), most classification of the features takes place here. Answer is B
You're right! The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
Very informative and interesting video, made it really easy for me to learn neural networks. Thank you
The correct answer is B- Error is always calculated at the output layer.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
It's difficult to make a video but thank you so much for making this and for clearly explaining it for us to understand! :)
The answer to the quiz question is B.
Thanks for this straightforward explanation of how neural networks operate.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
I'm in 9th Grade
its very useful for me
very well explained sir
And the Answer is OPTION B
Greetings from India !
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
@@SimplilearnOfficial Thx !
B: Error is calculated at each layer of neural network
Hi Hrushikesh, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The way u explained is so neat and clean, no loopholes. Thankssss
You're welcome! Thank you for watching!
Thanks for your Video
The Answer is “B/ error is calculated at each layer”
It’s not calculate the error
"Hello, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Good evening sir, Thanks to Mr.Simplilearn for your teachings on neural network.
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
Nice video sis/bro
Also can a neural network can be used to find a best combination of parameters from multiple parameters
Like if parameters (1,2,3,4,5,6,7,8) are fed as input , can it identify the best combination( pair of parameters) like (1&2, 1&3, 1&7, 2&8, 3&5, 7&1) for efficient performance of a system
I was given this project for fuel cell performance estimation by inputing its operating and design parameters and finding the best combination which influences the performance most
Can it be done in MATLAB?
Pls show some light
Excellent explanation!! Thanks a lot! :D
Glad it was helpful!
All your videos are Awesome, you made my college exam's easier, these videos give clear cut meaning and understanding in very short time, So please do more videos
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
Damn, the quality of explanation is awesome.
We are so grateful for your kind words. Also, subscribe to our channel and stay tuned for more videos. Cheers!
Crazy to think that we’re a natural neural network thats learning how to make artificial neural networks.
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
Answer is B. Error is calculated at each layer of the neural network.
This statement is wrong since errors could only be rectified using back-propagation that occurs during the training of the neural network.
Thank You Simplilearn.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
the false is because the Error is calculated in output layer
and great job to simpil this consept
I have a question, How the weights are calculated? Also, how to know the value if bias for each neuron in the hidden layer? thank you.
Im no expert but let me try give it a shot.. initially, weights and biases are set at random..in python programming, u can use numpy.random to do this.. as your neural net process the inputs and give and output when training it, it will check how far is it from what the output supposed to be (loss value).. based on this, your neural net will adjust the weights and biases of each neurons in the hidden layer until the loss value nears zero or becomes static or the iterations assigned is completed.. depending on how well your model perform, u may have to manually changes some parameters such as number of neurons, learning rate, number of hidden layers, activation function etc.. im not sure if u can print the value of the final weight and biases of the neuron tho.. hope that helps
@@edu1113 Thanks for your valuable input!
"Hi Faris,
Here are two blogs that will help you understand how weights and biases work in a neural network:
hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491
medium.com/coinmonks/the-mathematics-of-neural-network-60a112dd3e05"
@@SimplilearnOfficial thank you for the video, the question and your response to the question. Much appreciated!
Do one for how chatgpt works.
I think that is how chatGPT works
Really good video with great graphics, narration and animation. Thanks!
Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )
Super Explanation.....🥳🥳🎊🎉🔥🔥🔥🔥👏👏👏👏👏Me expecting more and more animated videos....like this ......
WooHoo! We are so happy you love our videos. Please do keep checking back in. We put up new videos every week on all your favorite topics. Whenever you have the time, you must also check out our blog page @simplilearn.com and tell us what you think. Have a good day!
You yes you who thought this video thanks a lot god bless you 😁
You're welcome 😊
The answer is B because the error is calculated at the output layer after the output values and expected values are compared.
Hi Luis, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs500/ $10. The answer to the question is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
@@SimplilearnOfficial What a great new, it's awesome that you reward your followers, thank you very much.
@@SimplilearnOfficial djandroide97
You are very welcome. Do show your love by subscribing our channel using this link: th-cam.com/users/Simplilearn and don't forget to hit the like button as well. Cheers!
I don't understand what the values of x_i would be. Is it an intensity value for pixel brightness and so you're trying to detect if some pixel is bright or not? then couldn't you just investigate the edges of the image (i.e. where there's a gradient between pixels)? I don't understand.
Better than an hour lecture!
So this is an old video so I doubt I'll get a reply, but could someone please explain why the weights vary and how the value of the weigh s are assigned? Thank you
abdullah ihsan the weight is essentially the probability each neuron will give the network that the pattern is what it is looking for and the weight is measured out of 1 So in this case if a square is input, and if neuron x1 is absolutely sure that the pixel it is looking at belongs to a square, it would give it a weighting of 1. If it is somewhat sure for example it would give it a weighting of 0.5. Over time, the better the neurons and network layers get and the more training the network gets to identify patterns correctly from input images/data, the more accurate the output results
"Hi Abdullah,
Here is an article that will help you understand how weights are assigned to a neural network.
towardsdatascience.com/weight-initialization-techniques-in-neural-networks-26c649eb3b78"
The Answer is “B/ error is calculated at each layer”
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Definitely B !! Thanks for the Video!! Liked, shared and now following!
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Great video man, in 5 minutes you just explained everything in such a simple way
We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
It's funny that it's been three years and now I'm watching this video myself
Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )
Great video!
Two questions I have...
Q1- is there an error in the formula when the weight etc is being discussed? The formula reads "(X1 * 0.8 + X3 * 0.2) + B1" should it not be (X1 * 0.8 + X2 * 0.2) + B1?
and
Q2 - does the neural network add each of the inputs in one formula? I.e. "(X1 * 0.8 + X3 * 0.2 + X3 * 0.1...) + B1 + B2 + B3..." and so on?
Thanks!
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
@@SimplilearnOfficial bot ans...hahahha
Amazing video explained in 5 minutes! Love it
Glad you liked it!
You r absolutely marvelous Sir!!!
Good explanation 👍👍
Thanks a ton!
which software is used to make such a interactive animation? thank you.
Hi Long, we use Scribe and Aftereffects to make these animations. Thanks.
Hi Long, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Hii Long, have you got the reward from simplilearn. Bcz i was also selected for the prize but have not got that yet.
Hi Nayan, we are sorry about that and we didn't get any response from you either. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10.
B as the error is calculated at the end of the NN, as error is original value - predicted value.
Hi Satyajit, thanks for your reply! We will give out the answer to the quiz coming Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks a lot, clear explanation!!
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
Thanks for the Video! The answer is B.
B B B! btw, good video!
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
the answer is B
We're thrilled to have been a part of your learning experience, and we hope that you feel confident and prepared to take on new challenges in your field. If you're interested in further expanding your knowledge, check out our course offerings in the description box.
Ans is b
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sana, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Good one. A lovely real life application of Math.
Glad you liked it!
Great explanation -- if you already understand the subject. Otherwise, not so much...
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
its absolutely B....
Hi Nikhil, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Ans is B
Hi Tharun, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Tharun, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
How can i claim this winning voucher??
Thank u for selecting me as one of three winners
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
You explain concepts better than experts like Geofrey Hinton
Thanks for watching our video and sharing your thoughts. Do subscribe to our channel and stay tuned for more. Cheers!
Thanks a lot for this explaination.This is really awesome...
You are most welcome
The Answer to the question is
: B
Hi Nayan, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model.The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
@@SimplilearnOfficial pubgopen@gmail.com
Answer is D option.
Lol. Really dude?
Hi Sandeep, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sandeep, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
The answer is B because error gets calculated only after comparing the predicted output with the actual output in the training process.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
B. Thank you for the great explanation.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Option B
Hi Sahitya, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Congratulations, you got the correct answer and have been selected as one of the 3 lucky winners of our contest. Please reply with your email ID to this comment to receive your Amazon gift voucher worth Rs 500/ $10. The answer to the question is given below:
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Option (B)
Hi Sameed, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Sameed, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
none could explain it any better!
Keep learning with us .Stay connected with our channel and team :) . Do subscribe the channel for more updates : )
B, I Loveee thiss, thanks for explaining so nicelyyy .. subscribed!!
Thanks for subbing!
C is correct
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi Avijeet, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
@@SimplilearnOfficial ANSWER IS B
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Answer of the quiz is - B
Hi, thanks for your reply! We will give out the answer to the quiz next Wednesday, 26th June 2019. If your answer is right, you could be one of the 3 lucky winners to grab Rs 500 or 10$ worth Amazon voucher. Stay Tuned!
Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network. This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
Thanks for making this video. I don’t remember what professor was teaching in my class😀
Thanks a lot for this informative video u are great
Our pleasure
Nice Explanation
We're so glad that you enjoyed your time learning with us! If you're interested in continuing your education and developing new skills, take a look at our course offerings in the description box. We're confident that you'll find something that piques your interest!
Thanks sir you are genius
Most welcome
very good explanation i liked it very much but how they are calculated plz reply
Hi Manish, For a detailed understanding of how neural networks work, please check this link: th-cam.com/video/ob1yS9g-Zcs/w-d-xo.html
Thank you for watching!
B is the answer after the forward propagation we are suppose check the error by the submisson of 1/2 (taget ouput - actual ouput ) ^2 .
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Thanks for the explanation 👍😊
Happy to help!
correct option is B, as an erroe is calculated at the end in the output layer and if its layer the information is sent and processed again
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
You are Amazing sir
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Amazing Very nice Explanation
Thanks and welcome
Insightfull video! Thankyou
Hey Jordi, thank you for appreciating our work. We are glad to have helped. Do check out our other tutorial videos and subscribe to us to stay connected. Cheers :)
Thank you so much Sir for your video...
I think I can write this answer in my tomorrow paper...
And Answer Is B.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Dude your video was great 👍
Thank you for choosing us as your learning partner. We are thrilled to hear that you enjoyed your experience with us! If you are looking to expand your knowledge further, we invite you to explore our other courses in the description box.
Thanks your useful video....
You’re welcome 😊
I am a new subscriber and I think that is an amazing video... Thank you so much
Thanks for subbing!
B is the answer as the error is calculated at the output layer and based on the errors the backpropagation takes place to adjust the weights
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Best Explanation.Thanks For Such Valuable Information.Keep It Up.
Hey, thank you for watching our video. We are glad that you liked our video. Do subscribe and stay connected with us. Cheers :)
Thank you!
You're welcome! Glad you enjoyed it!
Wow its a good video thankyou the answer of the quiz is B
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
B. Error is calculated at the very end, right before any backward propagation takes place.
Somehow ended up here thanks to one of my Neuroscience classes... cool
B. Error is calculated at each layer of the neural network. Because in fact, it's at the end that we see the error.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
b) is wrong alternative ( Errors are calculated once at the end of forward progression, to initiate backward progression)
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
nice video ,,,,please upload video of PSO-CNN Hybridization
We are glad you found our video helpful. Like and share our video with your peers and also do not forget to subscribe to our channel for not missing video updates. We will be coming up with more such videos. Cheers!
Thanks, Good explaination... Ans should be C
Sorry! The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The back propagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error. The competition period is now over. Thank you for watching and participating!
A is the answer of that quiz, thanks .
"Hi Ahmad, we are sorry to say that you got the wrong answer but in any case, the contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
The answer is B right. Is this forward and backward propagation of neural networks follows order?
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
Excellent!
Thank you! Cheers!
Answer is B: Error is calculated at each layer of neural network. It is done at output layer.
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
The answer is B!!!
(Can I have a voucher please?)
"Hi, you got the right answer. Kudos.
The contest is over and winners have been announced in our community and also mentioned in the top comment of the video as well. Thanks for your participation.
The correct answer to the quiz is Option B.
Explanation:
In a neural network, the error in the model is always calculated after finding the predicted output, i.e., at the output layer of the network.This predicted output is compared with the actual output of the model. The backpropagation algorithm is performed on the network, and the weights are optimized to reduce the error in the model. This process is repeated multiple times to get the final output, which has the least minimum error.
"
I have one problem. is every ML model has a neural network? If not please give me a summary of the ML Model with ANN & ML Model without ANN