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I am a young Ai and machine learning engineer from a IIIT and your videos are like food for me if i don't eat then I can't live .Great explanation ... finally I commented after watching tons of your videos daily . Salute to your spirit sir you will reach 10 M subs soon cause AI and ML is growing exponentially and your videos in this direction in serving as no. 1 you tube channel for simple explanations on Practical AI,ML coding and more people will join with you soon and soon...
Your tutorials are truly outstanding, surpassing many paid online courses. I want to express my deep appreciation for the invaluable support they've offered. Your detailed explanations of each code line have been incredibly helpful, particularly when I'm teaching machine learning to my students. Your videos provide a level of comprehension and utility that distinguishes them from other machine learning resources. Your efforts are greatly appreciated... Cheers!!!!!!!!!!!!!!!!💥💫💢
Indeed you are an excellent tutor. Your efforts are greatly appreciated .I am fun of you. I AM an AI and machine learning outreach ,you pave me the way .Thanks a lot for you support
This was my first to machine/deep learning as i had to do an assignment. Still i understood it vey well and now I'm able to do CNN on my own. Thanks to the tutor :)
Mr. Modi (Mr. Patel) is one side and rest Opposition (Data Science TH-camr) is on the other side. I really envy you (ONIDA TV) and you command that envy with your highest excellence. I am a retired Sr. Citizen and love data science (not because I understand it) but because of the amazing things that Amazon and Tesla and Google are doing.. Please keep going..and may God give you a very long life..
how come you tube did not recommend me this way before. Your videos are just perfect for people who want to learn Deep Learning and want to overcome the fear of AI
thank you soo much. from the knowledge i gained from this video, i decided to also increase the number of epochs in the first network(ann) from 5 to 10 and that led to a slight increase in the training accuracy(0.49 to 0.54). and for the cnn i intentionally decided first use the SDG optimizer and later the adam which also gave two different but better results than the ann. i also adjusted the epochs in each case. this has given me some more ideas to play around with, with regards to this model. once again thank you for bn such a great teacher
me too i searched for my issue accuracy was 10 % and no increase however i increased hidden layers epochs , but what help me is changing the softmax to sigmoid and the number of hidden units it was 4 on my project here i found it 3000 , it increase my accuracy too , but based on what he choosed 3000 and 1000 hidden units ?
@@ahmedhelal920 More hidden units will recognize more patterns and more features, which will help if your images have many patterns and objects. It is always recommended to use more hidden units on layers and decrease it after every layer to reach a better solution.
I found something very important. When you reshape your y into 1 dimension, save it in a different variable and use the original one (2d) in the training and test process. Otherwise, the results change a lot
Your videos are very good...you explain every line of code...it really helps me a lot to teach ML to my students...your videos are even more useful then other ML videos...👌😊
yes it might. you can try adding them. sometimes too many layers will overfit a model and while accuracy improves on training set, on test set it might perform poorly. You can use regularization techniques such as adding dropout layer to tackle these issues partially
Sometimes i have thoughts in my mind that is this really happening or is this valuable( i am not judging or not even assuming) as this type of course are paid and with huge amount of money with high demand but how you can give this for freeee ??????? How sir how ??? Hats off👍👍👍👍👍and big thanks 👌👌👌 🙏🙏🙏🙏 I think this learning won't be stopped ever from you.
ha ha... that's a nice way of appreciating my work Ajay. Thank you. Well this course is not free, the fee you need to pay is share this with as many as you can (via linkedin, watsapp, facebook groups, quora etc) :)
sir I am really appreciated, the way you teach all the concepts related to CNN, and how to build it, sir how can get more accuracy using Keras tuner, please make a video on that.
the VIdeo is really help full, but you should have also to show where you data set is store, because I am accessing but unable to access my data set from my computer
You are doing an amazing work.. I really get intrest in ml after watching your video explanation.. Sir I'm work on project "image classification using deep neural network" The data set is *CIFAR 10*. Paper on which I'm working it already has 80.2% of accuracy . So by using deep neural network algorithms can I make accuracy beyond 80%
Thanks a lot for your great courses, is it possible for you to explain my question? How should we add non-image features to our CNN model (features like cat and dog prices) to our flatten layer? Does the CNN model new added features belong to which input image?
Hello, can you please guide for the K-NN, MLP, CNN, Decision Tree, K-Mean Clustering, regression to solve this CIFAR-10 dataset problem. And compare the accuracies for each of the methodologies used.
Great video. I was hoping you'd visualize the CNN kernels so we could see what they looked like. You specified 32 of them. Does this mean that all 32 are used in every image, and thus are meaningful in every case? that is, you won't have one what has a koala's eyes because the input images also include, say, rocks, buildings, and GPU cards?
Thank you for the awesome tutorial. I have one question. Is there a way so I could give a path to one folder and then it would classify images which are in it using this model?
Hi, thanks for the clear explanation. I was wondering why you did not use softmax activation function in the last layer instead of sigmoid? As far as I know, softmax is preferred in multiclass problems (like in this case) and sigmoid is used for binary classification problems. Let me know and I appreciate your answer in advance.
IT's not the case Dhaval, that ANN is performing badly, if you change the y_train/y_test to categorical and use loss='categorical_crossentropy' it's giving 91% accuracy. I feel CNN will perform certainly better but we may need much higher dataset.
Thank you so much for this great tutorial. It is really helpful. I have a question, you used 'sparse crossentropy' in prediction and it's supposed to return the class number but the output of y_pred is an array of the probability of each class, and to get the predicted class we used argmax function to get the index of maximum value?
Hello Bassem, "Sparse categorical cross entropy" is the loss function to be used when the actual output Y in the dataset is not in the one hot encoded format. And, sir has used "softmax" as the final activation function in the code showed in the video. It is because of this function that the final output, y_pred is an array of the probability of each class. Hence, finally in order to get the index position of the maximum probability value, which is typically the output class predicted by the CNN model, sir has used the np.argmax function.
haven't watch the entire video yet. Is there a way to type in a command which looks for statistical outliers in the data that don't match anything well , so they can be eliminated to improve the model?
@codebasics plz make a video on implementation of transfer learning models like inception v3 vgg16 resnet152 for image classification. Plzzzzz i am waiting for ur response. And can u make one more video on feature concatenation of all models and ensemble method of all
I hope you are doing...I had an assignment of image classification and we were suppose to make a confusion matrix I searched on your channel ...and couldn't find any related to confusion matrix. Please make one on that
great tutorial and I enjoyed it! However, what if I want to classify new images which not in CIFAR dataset, for an instance, I want to classify a butterfly breed. Any suggestion?
@@codebasics Thanks for your reply. Can you give me some clue or code on how to add the new images into the existing dataset to re-train the model with CNN? Do I need to annotate those images if those are not in the cifar, imagenet, coco and google open images? I see that in your last video mentioned about these data source, however, I didn't see any info how to add and train custom dataset.
Check our Deep Learning Course (in PyTorch) with Latest, Industry Relevant Content: tinyurl.com/4p9vmmds
Check out our premium machine learning course with 2 Industry projects: codebasics.io/courses/machine-learning-for-data-science-beginners-to-advanced
sir plese plese reply i am doing a project on pcb defect detection using cnn model please help me out i am not getting it please help me
From Brazil, you are the best ML teacher!!! Thank you.
Thanks Luciano for your kind words
Excellent tutorials much better than many highly paid course floating online..Thanks a lot sir ..your videos helped me lot ...
I am a young Ai and machine learning engineer from a IIIT and your videos are like food for me if i don't eat then I can't live .Great explanation ...
finally I commented after watching tons of your videos daily . Salute to your spirit sir you will reach 10 M subs soon cause AI and ML is growing exponentially and your videos in this direction in serving as no. 1 you tube channel for simple explanations on Practical AI,ML coding and more people will join with you soon and soon...
Ha ha .. thanks for your kind words of appreciation my friend :)
Your tutorials are truly outstanding, surpassing many paid online courses. I want to express my deep appreciation for the invaluable support they've offered. Your detailed explanations of each code line have been incredibly helpful, particularly when I'm teaching machine learning to my students. Your videos provide a level of comprehension and utility that distinguishes them from other machine learning resources. Your efforts are greatly appreciated... Cheers!!!!!!!!!!!!!!!!💥💫💢
You are so much better than my university tutors :-D Thanks a lot for your help!
From South Korea, Learning Much Faster, Accurate than Univ. Thanks
🤗🤗🙏
We Asians are for us ❤
lol
I started to learn ml after getting inspirations from your videos. Thank you !
Happy to hear that sabrina!
@@codebasics lol lo
Plpp
Pl
@@codebasics pl
as you teach all concepts even a primary student can understand it easily. Seriously big fan of your teaching style
the important CNN concept is explained in superb and simple to understand , Thanks a lot
Indeed you are an excellent tutor. Your efforts are greatly appreciated .I am fun of you. I AM an AI and machine learning outreach ,you pave me the way .Thanks a lot for you support
someone give this man a life elixir, he must give this knowledge for all the future generations
Exciting Times!! May this series long continue😁
yes it will. My goal is to cove all the topics and make this your one stop place for deep learning
Excellent tutorials much better than my professor! You are the best! thank you so much! your videos helped me a lot....
This was my first to machine/deep learning as i had to do an assignment. Still i understood it vey well and now I'm able to do CNN on my own. Thanks to the tutor :)
Mr. Modi (Mr. Patel) is one side and rest Opposition (Data Science TH-camr) is on the other side.
I really envy you (ONIDA TV) and you command that envy with your highest excellence.
I am a retired Sr. Citizen and love data science (not because I understand it) but because of the amazing things that Amazon and Tesla and Google are doing..
Please keep going..and may God give you a very long life..
Thank you sir! Teaching is also a skill and you nailed it!
how come you tube did not recommend me this way before. Your videos are just perfect for people who want to learn Deep Learning and want to overcome the fear of AI
Such a Good Content.
I am really exciting for upcoming videos.
Glad to hear that
You are the best teacher of mine. I'm grateful to you always. Thanks a lot, sir.
Zeenat, thanks for you kind words
REALLY A GOOD VIDEO , i finally understood implementing CNN using CIFAR10
Amazing tutorial, thanks a lot for sharing! Saludos desde Argentina! 🇦🇷
From Bangladesh...very helpful ❤
Thank you...this course has been inspiring
I love your way of teaching
thank you soo much. from the knowledge i gained from this video, i decided to also increase the number of epochs in the first network(ann) from 5 to 10 and that led to a slight increase in the training accuracy(0.49 to 0.54). and for the cnn i intentionally decided first use the SDG optimizer and later the adam which also gave two different but better results than the ann. i also adjusted the epochs in each case. this has given me some more ideas to play around with, with regards to this model. once again thank you for bn such a great teacher
me too i searched for my issue accuracy was 10 % and no increase however i increased hidden layers epochs , but what help me is changing the softmax to sigmoid and the number of hidden units it was 4 on my project here i found it 3000 , it increase my accuracy too , but based on what he choosed 3000 and 1000 hidden units ?
@@ahmedhelal920 More hidden units will recognize more patterns and more features, which will help if your images have many patterns and objects. It is always recommended to use more hidden units on layers and decrease it after every layer to reach a better solution.
Very good explanation with a clear easily understandable video. Thank you for your tutorial. Loved it.
Hi Thank you for all your tremendous work you make fall in love with Machine learning. don't you dare to stop;) Thank you so so so much.
Thanks for your kind words khan ☺️ and yes now after reading your comment I am not going to stop 😉
@@codebasics bless you.
thank you from Finland
how to split the image data into training and testing in folders
Thank you sir, excellent explanation
No one in universe can teach like this
Thanks zain for your kind words
Thank you, It is a great tutorial😍 on CNN
You are superb in teaching. Please make video on how to deploy such trained models to production.
Excellent explanation. 👏
You are really inspirational and have so much to idolize. Thank you!
Glad it was helpful!
Excellent demo, saved my time.
Awesome really like the face to face introduction
Glad you like it
really good explanations. thanks for your great help
All the way superb!!!! All videos.
great job sir.....keep making videos love to watch and learn from your videos
Excellent content! Thank you very much.
I’m from Taiwan. It’s really helpful
Glad it was helpful!
For digits: ann gives 90%, cnn gives 99+% on train dataset and 99% on test data, thanks sir
Very lucid explanation
Tq so munch sir for continuing this series amazing content supreb nice explantion
You're most welcome sathiya
very nicely explained brother. Loved the teaching style and followed the explanation
😊😊👍
Thank you a lot! You helped me with my project!
Glad it was helpful!
Thank you very much sir for this 😊
I found something very important. When you reshape your y into 1 dimension, save it in a different variable and use the original one (2d) in the training and test process. Otherwise, the results change a lot
Why results change alot?
Your videos are very good...you explain every line of code...it really helps me a lot to teach ML to my students...your videos are even more useful then other ML videos...👌😊
Glad you like them!
Tq u 💯 much sir, this video is very helpful.😍❤️🌹👍🥰🇮🇳
Your approach is very well. You can explain the topics so well and easy to understand the complex topic.
Glad to hear that, I am happy this was helpful to you.
Realy sir I like your teaching way
Thanks and welcome
Your classes are really beginner friendly and I have a doubt will adding more layers improves the accuracy
yes it might. you can try adding them. sometimes too many layers will overfit a model and while accuracy improves on training set, on test set it might perform poorly. You can use regularization techniques such as adding dropout layer to tackle these issues partially
Thank you so much! Your tutorials are very helpful
Glad you like them!
thanks a lot sir for your explanation. i got accuracy of 98.97% using cnn model
Great job
Can you share with your code?
sir when i running all this code in google colab my accuracy is 0.1 and not increasing in cnn case also.
Thank you for the efforts you put in all these vedios, it is giving us a clear image of what is happening in each part. Thanks alot
You are the best by far
I am happy this was helpful to you.
very nice videos thank you so much bro :)
Thank you so much for detailed tutorial. Can you please make a video on Object detection? Specially Faster RCNN and Yolo models.
Sometimes i have thoughts in my mind that is this really happening or is this valuable( i am not judging or not even assuming) as this type of course are paid and with huge amount of money with high demand but how you can give this for freeee ???????
How sir how ???
Hats off👍👍👍👍👍and big thanks 👌👌👌
🙏🙏🙏🙏
I think this learning won't be stopped ever from you.
ha ha... that's a nice way of appreciating my work Ajay. Thank you. Well this course is not free, the fee you need to pay is share this with as many as you can (via linkedin, watsapp, facebook groups, quora etc) :)
@@codebasics will do it definitely
✌✌Long live developers👍👍👍
Very nice explanation on CNN....
how you can simplify such complex topics ? You must be having rich experience in this field...😊
Excellent video. Thank you
I am happy this was helpful to you.
great job sir.....
Thank you
model.evaluate:10000/10000 [==================] - 1s 57us/sample - loss: 0.0275 - accuracy: 0.9910
The ann model refuse to run. It saying keras has no attribute sequential
great sir thank you
permission to learn sir. thanks you
Thank you 🙏
Glad it was helpful!
sir I am really appreciated, the way you teach all the concepts related to CNN, and how to build it,
sir how can get more accuracy using Keras tuner, please make a video on that.
Nice tutorial sir. Can you create a chatbot using ANN? I would like to know how you will test that. Thanks!
thank you very much sir
Really nice video...its helped me lot...
I want you to start Audio, Video processing tutorial also because I like it your teaching skills.
Glad it was helpful!
the VIdeo is really help full, but you should have also to show where you data set is store, because I am accessing but unable to access my data set from my computer
GREAT SIR
Thanks for nice explanation. Easy to understand the concepts. Can you make video for region CNN and faster R CNN?
You are doing an amazing work.. I really get intrest in ml after watching your video explanation..
Sir I'm work on project "image classification using deep neural network" The data set is *CIFAR 10*. Paper on which I'm working it already has 80.2% of accuracy . So by using deep neural network algorithms can I make accuracy beyond 80%
Could you explain in detail about the reshaping process, on why its necessary ?
Thanks a lot for your great courses, is it possible for you to explain my question? How should we add non-image features to our CNN model (features like cat and dog prices) to our flatten layer? Does the CNN model new added features belong to which input image?
Hello, can you please guide for the K-NN, MLP, CNN, Decision Tree, K-Mean Clustering, regression to solve this CIFAR-10 dataset problem. And compare the accuracies for each of the methodologies used.
Great video. I was hoping you'd visualize the CNN kernels so we could see what they looked like. You specified 32 of them. Does this mean that all 32 are used in every image, and thus are meaningful in every case? that is, you won't have one what has a koala's eyes because the input images also include, say, rocks, buildings, and GPU cards?
Thank you for the awesome tutorial. I have one question. Is there a way so I could give a path to one folder and then it would classify images which are in it using this model?
Yes you can use tensorflow dataset pipeline for that watch TF data pipeline tutorial in this same playlist
@@codebasics Thank You, I'll definitely watch it.
So are you executing everything in a GPU?
please explain that, why you reshape x_train in exercise, and also change input_shape in conv2D
Hi, thanks for the clear explanation. I was wondering why you did not use softmax activation function in the last layer instead of sigmoid? As far as I know, softmax is preferred in multiclass problems (like in this case) and sigmoid is used for binary classification problems. Let me know and I appreciate your answer in advance.
I had the same question, finally found someone with the same doubt
Where can I get the dataset from?
amazing
IT's not the case Dhaval, that ANN is performing badly, if you change the y_train/y_test to categorical and use loss='categorical_crossentropy' it's giving 91% accuracy. I feel CNN will perform certainly better but we may need much higher dataset.
Where is the jupyter notebook code link? Plz share
Thank you so much for this great tutorial. It is really helpful. I have a question, you used 'sparse crossentropy' in prediction and it's supposed to return the class number but the output of y_pred is an array of the probability of each class, and to get the predicted class we used argmax function to get the index of maximum value?
Hello Bassem, "Sparse categorical cross entropy" is the loss function to be used when the actual output Y in the dataset is not in the one hot encoded format. And, sir has used "softmax" as the final activation function in the code showed in the video. It is because of this function that the final output, y_pred is an array of the probability of each class. Hence, finally in order to get the index position of the maximum probability value, which is typically the output class predicted by the CNN model, sir has used the np.argmax function.
haven't watch the entire video yet. Is there a way to type in a command which looks for statistical outliers in the data that don't match anything well , so they can be eliminated to improve the model?
Dear Sir, I have a data-frame with shape: 6500 rows and 146 cols. It is not a 3D data. How can I apply input_shape parameter to use CNN model?
Brilliant!
sir how i convert the prediction into csv file with column names (filname and label)
convert to pandas dataframe and just execute - df.to_csv('df.csv')
@codebasics plz make a video on implementation of transfer learning models like inception v3 vgg16 resnet152 for image classification.
Plzzzzz i am waiting for ur response.
And can u make one more video on feature concatenation of all models and ensemble method of all
I hope you are doing...I had an assignment of image classification and we were suppose to make a confusion matrix I searched on your channel ...and couldn't find any related to confusion matrix. Please make one on that
My Prof. does not teach anything about coding but gives bunch of homework :( But it was healing !
great tutorial and I enjoyed it! However, what if I want to classify new images which not in CIFAR dataset, for an instance, I want to classify a butterfly breed. Any suggestion?
For that you need to add images of butter fly breed in this dataset and train the model again.
@@codebasics Thanks for your reply. Can you give me some clue or code on how to add the new images into the existing dataset to re-train the model with CNN? Do I need to annotate those images if those are not in the cifar, imagenet, coco and google open images? I see that in your last video mentioned about these data source, however, I didn't see any info how to add and train custom dataset.