That might be the perfect age to start! you know much more than any of us about life, logic and human behavior - just imagine how many wonderful things you can teach your AI models about the world! 😀 And thank you so much for your lovely comment! 😊
I've watched many Python tutorials on TH-cam, some good, some not so much...but I really love how yours are truly "simplified" and easy to follow! Amazing for total beginners like myself! Subscribed!
Love how you explained it to me. I usually get discouraged by tutorials that make it seem very complex but you have made it easily digestible for me. Thanks
This has to be the simplest and easiest to understand tutorial on perceptrons in Python. All other tutorials hit you with linear algebra right from the start but this looks at concepts and then code. Well done.
I listend to your episode on the Superdatascience podcast To say things in simple terms, you are GREAT at simplifying things! Now I understand my homework, thank you!
I'm loving this AI and ML series! I don't think I have the brain for Math, but I still find ML and in particular DL fascinating, so will continue watching and hope that something you teach sticks. 🙂
I'm still very new to Python and I don't recall exactly how I stumbled upon your tutorials - but this got me hooked. For one, my brain is absolutely on fire right now, thinking of things I'd like to automate or outsource to a digital brain. Speech recognition based on spectrograms. Perfecting pastry recipes based on scientifically analyzed chemical/physical interactions between ingredients. Or translating legislative econo-babble into sensible, comprehesible bullet-points. IF that's possible. And two: I love Python's syntax. So elegant and concise, especially with regards to variables. My entry drug was C++ but these days I dabble in webdev these days, and 3 out of 10 times I'll forget the mandatory $ in PHP. And don't even get me started on --var ;)
hahaha nothing compares to the simplicity of Python! it's like plain English with a bunch of ":" in the end of some sentences! 😉And I'm so happy that Machine Learning inspires you!! we are only beginning to understand the impact of AI technology as it can improve every single aspect of our lives (or destroy them - if only a few will possess its power and use it to control the many) There's absolutely no limit to what we can achieve with AI and many brilliant ideas haven't been implemented yet - so we're witnessing the very beginning of this technological revolution - what a time to be alive!!! 😁
@@PythonSimplified That last sentence seems familiar. Are you, by any chance, quoting Two Minute Papers' Karoly Zsolnai-Feher? His videos on AI based simulations are just pure brain candy. In any case, yeah, the sky is the limit, it seems - and I'm exactly the type that goes down the weirdest paths possible, and pushes on even after the path ended. Case in point: why would anyone make music videos in a point&click adventure game engine? Don't know. Don't care. It's fun. Though not as fun as doing that in PHP and HTML. Also, speaking of languages, are you a fellow Central/Eastern European? I might be totally off, but the vowel sounds and the intonation patterns remind me of Slavic languages - very clear and enunciated - and the dark L makes me look to the former Eastern bloc. It's a weird question, I know - but my academic background is in linguistics. Plus I'm weird :P
I really thank TH-cam for suggesting this video. This video is just perfect (simple and very helpful). This is how Tutorials should be. Thanks & Subscribed.
Thank you for this video!!!! You are the best teacher in my life! I couldn't this info about ML a long time. really thank you for your skills and time to record this video! Please continue to create videos about this topic!)
I suggest to make the input an actual input of the perceptron function, this would come in handy if you want to combine several perceptrons to a neuronal net later on. Also, with more neurons, you gonna need the scalar product much more often - maybe use the numpy dot function or implement the scalar product once and keep the rest of the code lean (avoid unnecessary for loops for readability) . Besides that, I think your short babystep videos on that topic might be a very good introduction for beginners. Keep it up.
Thank you so much for your feedback Frank! 😀 We will definitely use Numpy at a later stage to generate both input matrices and weights! Babysteps is indeed what I'm aiming for with this series, as I had such a hard time trying to learn the subject on Udacity 2 years ago and I was hoping to convey the concepts in a much more simple way with as many illustrated examples as possible 😊 Thanks again for your suggestions! 😁
Wow ! What a great video ! Seriously awesome content more, more, more please! TH-cam is grateful for content creators like you sharing valuable knowledge in a thorough concise format.
Thank you so much Rayan! 😀 I sure will!! Artificial Intelligence is an incredible journey! I'm so happy to see everybody enjoys the topic! 😁😁😁 The more we know about AI - the bigger the chance we can outsmart it in the future! 😉
I find that fancy scholars like using fancy words to showcase how fancy their terminology is... maybe if I was a fancy scholar - I'd have the same point of view 😅 Since I'm a student rather than a teacher - If I can't describe something simply that means I didn't fully understand it. The real life examples and illustrations helped me understand the concepts back in the day and now I'm hoping it will help others too! 😊
Thank you so much Pawel! 😀 Next AI video is about an Error Function and Gradient Descent, so we're finally getting to the more complex (but much more useful) stuff! 😉
You made the things more easier to understand I'm an high school student I've don't know what to do with these stuffs can you tell me how can i use these in my projects and applications of these and finally plzz tell me how to type fast suggest any video for me for typing
Hi Manoj! Machine Learning is the basis of many modern technologies: face recognition, self driving vehicles, robotics and much more! 😀 I've just started covering this subject so it's too early to understand how this video connects to the examples above - but very soon it will make more sense. Typing fast is a matter of experience, but try this website if you want to practice 😊: www.keybr.com/
Love your presentation style. Very light hearted and to the point. You make it very easy to learn even with the typos. P.S. I don't like snails either. 😋😃
hahaha thank you so much Alexander! I'm really happy you liked my presentation! 😀 I heard that if the snail is cooked (Escargot) - it tastes much better! I went for the hardcore raw version of it unfortunately 🤣
Very smart and well spoken! I subscribed! If you want to flex your brain, read up on integrated information theory. Our experiences amount to this irreducible complex of causal relationships. That multidimensional vector of that complex is the actual experience itself, qualia. This work was started by Francis Crick, of DNA discovery fame. It was further developed by christof koch and julio tononi.
if we add some randomness and test as long as there is a crossover of the step return >>> threshold = round(random.random(),1) >>> for i in range(10): x_input.append(round(random.random(),1))
>>> for i in range(10): w_weights.append(round(random.random(),1))
Hi Saurabh! 😀 We will definitely get there eventually! we need to go over the basics first though - as it's hard to talk about these topics before I defined "gradient descent" or "error function" or "learning rate", etc... so definitely stay tuned for the upcoming tutorials! 😉
1:38 is the input of snail really the same since some people taste differently? 🤔 For example, some people are wine tasters, beer tasters, and get paid a lot for it...they can detect even small changes in taste that others can't. *edit, yes I know it's just an example but I'm just saying...
I agree! their weights are probably adjusted much better than mine! 😉 even if the input is the exact same snail (cutting it down the middle I guess hahaha) - the process of interpolating the sensory data is different for each of us... which always reminds me of a dilemma from The Matrix - how do we know that the taste of chicken we sense on a personal level is indeed how chicken tastes? 😵
@@PythonSimplified I guess I would be too busy noticing the woman in the red dress to worry about the chicken 🤔but yes in that particular analogy you have variations in how people taste just like how some people have a better sense of smell than others. It's ok, it's just an example...every analogy can't be perfect right!
Hello Mariya, Is it recommend to code all the machine learning algorithms from scratch so that I can learn math behind it or just understand and start to code?
Hi Subhan! 😀 If you want to truly understand Artificial Intelligence - there's no way of avoiding the math aspect and dry coding the algorithms. However, it is more than possible to build AI without understanding a thing! (I know it because I've done it for my first project through Udacity! 😅 I absolutely had no clue about what I was doing but since I paid $600 for that course - I had to follow through 🤪 hahaha) So just using Python abbreviations and shortcuts is possible! you may even get some accurate models as a result (if you simply follow the Pytorch documentation for example). But optimizing your models and expanding them would be a MAJOR challenge! therefore I recommend to learn all the concepts first - and only then move on with coding! And I'm of course including code examples for each concept since it completes the cycle started by the illustrated examples 😊 But don't worry - I'll try to speed up with this AI series and get the information out ASAP and as simple as possible! 😉
How can a set of data be classified using a simple perceptron? Using a simple perceptron with weights w0, w1 , and w2 as −1, 2, and 1, respectively, classify data points (3,4); (5, 2); (1, −3); (−8, −3); (−3, 0).
Hey Mariya😍, maybe nowadays I'm thinking something more about machine learning for your awesome videos. Now my question is, "HOW BIG COMANY'S ARE USE MACHINE LEARNING IN THEIR COMPANY??" . Everytime almost everyone tell me that they use ML but how??? 🤔How to analyze the data for their customer?☺
Hi Mahin! 😀 Big tech companies are combining AI & Machine Learning directly inside the code of their service/software. For example: Facebook has over 12 years of user data collecting that consists of feed posts, photos, likes, comments, shares, tags, group conversations, and basically recording everything you do when using the app. Then they take all this data and they create a virtual version of you, which is an AI that is customized to you only. And then - based on your 12 years of history on Facebook - this AI determines what content to show you and what content to hide from you. The main objective is to keep you on the platform as much as possible and to select advertisements that are more suitable for you (products you are more likely to buy, causes you are more likely to support). This is just one example, but anywhere you see face recognition, speech recognition and things of that sort - AI is involved! If you want to see an example in code, check out my tutorial: th-cam.com/video/mzbJd0NhW2A/w-d-xo.html I don't explain much there, but it will give you an idea what is involved in combining AI inside your program. Since it's all created with code - you can include it almost anywhere! 😊
Very lucid explanation. It would have been good if you had shown how weights are updated over iterations. I would be making few videos on gradient descent and other related algorithms. You may give your feedback. Stay connected. I have subscribed your channel. Good job.
Thank you so much and welcome onboard! 😃 I have a bunch of other related videos, you can check out my take on Gradient Descent here: th-cam.com/video/jwStsp8JUPU/w-d-xo.html And I actually have an entire playlist of AI/ML/DL videos which you might also enjoy 😁: th-cam.com/play/PLqXS1b2lRpYTpUIEu3oxfhhTuBXmMPppA.html
Good 👍 In my opinion instead of 'return step(weighted_sum) ', we may have put here ' print(step(weighted_sum)), in order to see the output as well either 1 or 0 for each iteration, am I wrong?
![The moment we stopped understanding AI [AlexNet]](http://i.ytimg.com/vi/UZDiGooFs54/mqdefault.jpg)
Oh boy! At 73 yrs old I'm finally able to begin to understand Neural Networks. Thankyou so much.
That might be the perfect age to start! you know much more than any of us about life, logic and human behavior - just imagine how many wonderful things you can teach your AI models about the world! 😀
And thank you so much for your lovely comment! 😊
70 yrs mate, so you're not alone in this new adventure.
@@iansjackson way to late to be doing that now, enjoy your life
@@WxK_Riku different people enjoy different things
i never even thought about being 77 til i read your comment
I've watched many Python tutorials on TH-cam, some good, some not so much...but I really love how yours are truly "simplified" and easy to follow! Amazing for total beginners like myself! Subscribed!
Love how you explained it to me. I usually get discouraged by tutorials that make it seem very complex but you have made it easily digestible for me. Thanks
This made the Perceptron concept much more clear and easy to understand. Not a given on youtube. Thanks Python Simpliefied!
This has to be the simplest and easiest to understand tutorial on perceptrons in Python. All other tutorials hit you with linear algebra right from the start but this looks at concepts and then code. Well done.
I listend to your episode on the Superdatascience podcast
To say things in simple terms, you are GREAT at simplifying things!
Now I understand my homework, thank you!
I'm loving this AI and ML series! I don't think I have the brain for Math, but I still find ML and in particular DL fascinating, so will continue watching and hope that something you teach sticks. 🙂
Crystal clear Explanation with great code demonstration.
Thanks you.
An example with the added bias will be a good next video. Thanks for this one!
I love your simple explanations
Yey! Thank you so much! 😀
Exactly the kind of explanation I was looking for , THANK YOU!!!!!
Absolutely brilliant ! You are doing the world a favor truly by making these simple yet beautiful tutorials!
I'm still very new to Python and I don't recall exactly how I stumbled upon your tutorials - but this got me hooked. For one, my brain is absolutely on fire right now, thinking of things I'd like to automate or outsource to a digital brain. Speech recognition based on spectrograms. Perfecting pastry recipes based on scientifically analyzed chemical/physical interactions between ingredients. Or translating legislative econo-babble into sensible, comprehesible bullet-points. IF that's possible. And two: I love Python's syntax. So elegant and concise, especially with regards to variables. My entry drug was C++ but these days I dabble in webdev these days, and 3 out of 10 times I'll forget the mandatory $ in PHP. And don't even get me started on --var ;)
hahaha nothing compares to the simplicity of Python! it's like plain English with a bunch of ":" in the end of some sentences! 😉And I'm so happy that Machine Learning inspires you!! we are only beginning to understand the impact of AI technology as it can improve every single aspect of our lives (or destroy them - if only a few will possess its power and use it to control the many)
There's absolutely no limit to what we can achieve with AI and many brilliant ideas haven't been implemented yet - so we're witnessing the very beginning of this technological revolution - what a time to be alive!!! 😁
@@PythonSimplified That last sentence seems familiar. Are you, by any chance, quoting Two Minute Papers' Karoly Zsolnai-Feher? His videos on AI based simulations are just pure brain candy. In any case, yeah, the sky is the limit, it seems - and I'm exactly the type that goes down the weirdest paths possible, and pushes on even after the path ended. Case in point: why would anyone make music videos in a point&click adventure game engine? Don't know. Don't care. It's fun. Though not as fun as doing that in PHP and HTML.
Also, speaking of languages, are you a fellow Central/Eastern European? I might be totally off, but the vowel sounds and the intonation patterns remind me of Slavic languages - very clear and enunciated - and the dark L makes me look to the former Eastern bloc. It's a weird question, I know - but my academic background is in linguistics. Plus I'm weird :P
I really thank TH-cam for suggesting this video. This video is just perfect (simple and very helpful). This is how Tutorials should be. Thanks & Subscribed.
Yeeey!! Thank you so much for the incredible feedback!!! Welcome aboard! (There are plenty of AI tutorials coming in the next few months! 🙂)
Thank you for this video!!!! You are the best teacher in my life! I couldn't this info about ML a long time. really thank you for your skills and time to record this video! Please continue to create videos about this topic!)
Great tutorial! Seeing the perceptron and activation function makes it much more clear.
Best python chanel i have seen yet, thanks for help !
I suggest to make the input an actual input of the perceptron function, this would come in handy if you want to combine several perceptrons to a neuronal net later on. Also, with more neurons, you gonna need the scalar product much more often - maybe use the numpy dot function or implement the scalar product once and keep the rest of the code lean (avoid unnecessary for loops for readability) . Besides that, I think your short babystep videos on that topic might be a very good introduction for beginners. Keep it up.
Thank you so much for your feedback Frank! 😀
We will definitely use Numpy at a later stage to generate both input matrices and weights!
Babysteps is indeed what I'm aiming for with this series, as I had such a hard time trying to learn the subject on Udacity 2 years ago and I was hoping to convey the concepts in a much more simple way with as many illustrated examples as possible 😊
Thanks again for your suggestions! 😁
Love how simple you made it
This is the perfect level of difficulty for my high school computer science course. I hope you have many more planned!
Now why wasn't it explained like this to me before?
Great video
Wow ! What a great video ! Seriously awesome content more, more, more please! TH-cam is grateful for content creators like you sharing valuable knowledge in a thorough concise format.
The way you explained is awesome, the support of python code gives lot of Confidence in understanding the primitive of NN. Thank you
Wow... Ioved the way u explained it... I didnt have such clarity on the subject before...you are awesome.. Plz teach us more just like this ♥️
Thank you so much Rayan! 😀
I sure will!! Artificial Intelligence is an incredible journey! I'm so happy to see everybody enjoys the topic! 😁😁😁
The more we know about AI - the bigger the chance we can outsmart it in the future! 😉
@@PythonSimplified haha 😂 you are very smart... I'm sure after listening to you.. Wil be able to outsmart AI in the future. 🔥
fantastic teaching style....WELL DONE MAAM ....KEEP IT UP..👍👍👍👍
Simply the best tutorial. Well done and thanks for your time and help.
Very clear description. Thank you!
I like how you explained it to me the way youd explain to a kid. Thanks!
hahaha I believe it's the only way to explain such complex topics! you'll see how these oversimplified examples will be worth it in the end! 😂
the best tutorial in youtube
Thank you so much Mohamed! 😃
Great video! I LOVE YOU 😀 and yours videos too
every day my love increase for python :)... Anyway the way of your teaching is good and eye catching
Thank you so much! Glad to hear that! 😀
Amazing explanattion...really... i am learning a lot with you. You are clear and very pedagogic.. my respects from Argentina
Muchas gracias Matias! I'm very happy you like my explanations, trying to simplify everything as much as possible! 😀
Best regards from Vancouver!! 😁🍁
This was srsly interesting irdk y isn't machine learning being approached this way, simplicity and practicality at the same time
I find that fancy scholars like using fancy words to showcase how fancy their terminology is... maybe if I was a fancy scholar - I'd have the same point of view 😅
Since I'm a student rather than a teacher - If I can't describe something simply that means I didn't fully understand it. The real life examples and illustrations helped me understand the concepts back in the day and now I'm hoping it will help others too! 😊
Very easy to follow and bouns example closer to the end,
Great lesson as usual. I can't wait for a continuation.
Thank you so much Pawel! 😀
Next AI video is about an Error Function and Gradient Descent, so we're finally getting to the more complex (but much more useful) stuff! 😉
Amazing video my friend!. Very simple and I love it.
Thank you so much Dark Wolf, glad you liked it! 😁
simple but very helpful perceptron tutorial. You explained the topic very well!
Very Intuitive explanation with visualization. 👍👍👍
Thank you so much Pallavi! I'm so happy liked this tutorial! 😁
I Love Your Perspective and Presentations; My Lovely Unix Based Minx!!!
You Are My Favorite Programmer On The "Tube"!!!
Thank you so much for the incredible feedback! Super glad to have you onboard! 😀
Wonderful tuto!
Thank you so much for this video. I really enjoyed watching you code a perceptron.
that was amazing , and you explained it nicely ..
You look beautiful, and You explain beautifully, my professors failed to explain with this clarity, you did a really good job
Cool topic, another awesome video. Great job!
Thank you so much Derrick!! I'm super happy you liked it! 😁
Thank you very much. Your explanation is clear and understandable.
Thank you Ali, I'm happy you liked my explanation! 😊
Beauty and Brain at one place..
Wooow you are so amazing.
Thanks I found what I need.
Amazing tutorial ❤❤❤ Thank you sooooooo much .... you explained everything amazing and understandable and also thank youu for talking slowly ❤❤❤❤❤❤
Lessons are very good. I'm russian speaker and I understand almost everything. Besides that, blogger is pretty girl)
Super explanation. Thanks!!!
Thank you so much Jonathan, glad you liked it! :D
Thanks. you cover the basic with real world examples .
Absolutely! I find it's the best way to learn such complex concepts! 😀
You made the things more easier to understand I'm an high school student I've don't know what to do with these stuffs can you tell me how can i use these in my projects and applications of these and finally plzz tell me how to type fast suggest any video for me for typing
Hi Manoj!
Machine Learning is the basis of many modern technologies: face recognition, self driving vehicles, robotics and much more! 😀
I've just started covering this subject so it's too early to understand how this video connects to the examples above - but very soon it will make more sense.
Typing fast is a matter of experience, but try this website if you want to practice 😊:
www.keybr.com/
@@PythonSimplified tq python girl😅(sry i dont know the name)
@@manojkumark3934 hahahah It's Mariya 😁
Very useful, thanks a million!
Love your presentation style. Very light hearted and to the point. You make it very easy to learn even with the typos. P.S. I don't like snails either. 😋😃
hahaha thank you so much Alexander! I'm really happy you liked my presentation! 😀
I heard that if the snail is cooked (Escargot) - it tastes much better! I went for the hardcore raw version of it unfortunately 🤣
very hot explanation.
Thanks a lot
Really good video on forward propagation. Are you a ML engineer or Data scientist?
Thanks for sharing. You're amazing!
Thank you so much Michael! 😁
Very smart and well spoken! I subscribed!
If you want to flex your brain, read up on integrated information theory. Our experiences amount to this irreducible complex of causal relationships. That multidimensional vector of that complex is the actual experience itself, qualia.
This work was started by Francis Crick, of DNA discovery fame. It was further developed by christof koch and julio tononi.
Surely, you would want the x-input, weights and threshold to be arguments in your perceptron function, right?
Super helpful and fun!
Mariya, your videos are always fun and useful. Do you have any books that you recommend on python machine learning?
thanks for the video, I will try the ZIP function out and find out how it works.
Thank you Tobs, I hope you enjoyed your no chicken no goat dinner! 😀
this is so on point 👏🏼 thank you for the effort :)
this really helped! thank you for the great video💖
very good!! the best video that I see
awsome didatic
thanks for all what u share. very interesting
Thank you so much! I'm glad you're enjoying my tutorials! 😀
if we add some randomness and test as long as there is a crossover of the step return
>>> threshold = round(random.random(),1)
>>> for i in range(10):
x_input.append(round(random.random(),1))
>>> for i in range(10):
w_weights.append(round(random.random(),1))
Waooo u explain very simple way please make more videos on multilayer neural network.
thank you for the great explanation.
Thank you! Happy you liked it! 😃
Very well explained , Thanks
nice video 😉thank you very much සුපිරි
Thank you Hansaja! Enjoy! 😁
@@PythonSimplified are you know 'sinhala' language?
@@hansajajayasanka6270 unfortunately no, but it looks beautiful! 😊
@@PythonSimplified thank you 💜😋
@2:51 aren't the variables wrong way round. Shouldn't it show w2 and w1?
Hey please give some tutorials on model optimization, quantization and pruning
Hi Saurabh! 😀
We will definitely get there eventually! we need to go over the basics first though - as it's hard to talk about these topics before I defined "gradient descent" or "error function" or "learning rate", etc... so definitely stay tuned for the upcoming tutorials! 😉
Great explanation ! Very easy to understood.
eccelente spiegazione, lineare e semplicegrazie
Grazie mille Fabrizio!
why I can not find any page after clicking to your link, to get to your scrip?
I don't know at which part of the magic to look, the marvelous code or the marvelous face...
Thank you so much Ibrahim! 😊
@@PythonSimplified ❤❤
Please correct me if I'm mistaking, the list containing the weights (w_weights) needs to sum up to 1.
Nice vídeo, amazing editing.
Thank you so much Manuel! 😊
1:38 is the input of snail really the same since some people taste differently? 🤔 For example, some people are wine tasters, beer tasters, and get paid a lot for it...they can detect even small changes in taste that others can't.
*edit, yes I know it's just an example but I'm just saying...
I agree! their weights are probably adjusted much better than mine! 😉
even if the input is the exact same snail (cutting it down the middle I guess hahaha) - the process of interpolating the sensory data is different for each of us... which always reminds me of a dilemma from The Matrix - how do we know that the taste of chicken we sense on a personal level is indeed how chicken tastes? 😵
@@PythonSimplified I guess I would be too busy noticing the woman in the red dress to worry about the chicken 🤔but yes in that particular analogy you have variations in how people taste just like how some people have a better sense of smell than others. It's ok, it's just an example...every analogy can't be perfect right!
well done - thanks for sharing.
Hello Mariya,
Is it recommend to code all the machine learning algorithms from scratch so that I can learn math behind it or just understand and start to code?
Hi Subhan! 😀
If you want to truly understand Artificial Intelligence - there's no way of avoiding the math aspect and dry coding the algorithms.
However, it is more than possible to build AI without understanding a thing! (I know it because I've done it for my first project through Udacity! 😅 I absolutely had no clue about what I was doing but since I paid $600 for that course - I had to follow through 🤪 hahaha)
So just using Python abbreviations and shortcuts is possible! you may even get some accurate models as a result (if you simply follow the Pytorch documentation for example).
But optimizing your models and expanding them would be a MAJOR challenge! therefore I recommend to learn all the concepts first - and only then move on with coding!
And I'm of course including code examples for each concept since it completes the cycle started by the illustrated examples 😊
But don't worry - I'll try to speed up with this AI series and get the information out ASAP and as simple as possible! 😉
Thanks for sharing this tutorial
THis is an amazing video thank you so much
This is very interesting and multi applicable in CNN conventional nerual network.
Can you give me some insight into the VC dimension of positive bias perceptron?
How can a set of data be
classified using a simple perceptron? Using a simple perceptron with
weights w0, w1 , and w2 as −1, 2, and 1, respectively, classify data
points (3,4); (5, 2); (1, −3); (−8, −3); (−3, 0).
Really! wonderful!!!😊
So simplified thanks
Hey Mariya😍, maybe nowadays I'm thinking something more about machine learning for your awesome videos. Now my question is, "HOW BIG COMANY'S ARE USE MACHINE LEARNING IN THEIR COMPANY??" . Everytime almost everyone tell me that they use ML but how??? 🤔How to analyze the data for their customer?☺
Hi Mahin! 😀
Big tech companies are combining AI & Machine Learning directly inside the code of their service/software. For example: Facebook has over 12 years of user data collecting that consists of feed posts, photos, likes, comments, shares, tags, group conversations, and basically recording everything you do when using the app. Then they take all this data and they create a virtual version of you, which is an AI that is customized to you only. And then - based on your 12 years of history on Facebook - this AI determines what content to show you and what content to hide from you. The main objective is to keep you on the platform as much as possible and to select advertisements that are more suitable for you (products you are more likely to buy, causes you are more likely to support).
This is just one example, but anywhere you see face recognition, speech recognition and things of that sort - AI is involved! If you want to see an example in code, check out my tutorial: th-cam.com/video/mzbJd0NhW2A/w-d-xo.html
I don't explain much there, but it will give you an idea what is involved in combining AI inside your program. Since it's all created with code - you can include it almost anywhere! 😊
Hello, thanks for this video. I'm new to this field and I'll like to know if the code at the end is the python code for perceptron or a pseudo code
Informative 😎
Very lucid explanation. It would have been good if you had shown how weights are updated over iterations. I would be making few videos on gradient descent and other related algorithms. You may give your feedback. Stay connected. I have subscribed your channel. Good job.
Thank you so much and welcome onboard! 😃
I have a bunch of other related videos, you can check out my take on Gradient Descent here:
th-cam.com/video/jwStsp8JUPU/w-d-xo.html
And I actually have an entire playlist of AI/ML/DL videos which you might also enjoy 😁:
th-cam.com/play/PLqXS1b2lRpYTpUIEu3oxfhhTuBXmMPppA.html
You are a genius
Can we use it for logistic regression analysis?
Good 👍
In my opinion instead of 'return step(weighted_sum) ', we may have put here ' print(step(weighted_sum)), in order to see the output as well either 1 or 0 for each iteration, am I wrong?
Could the weighted inputs be in a dictionary or a hashmap?
Hi Ms.
I want to learn neural networks. Can you help me in learning it?
Thank you
thanks for the great content
You're absolutely welcome Joseph, I'm glad you liked it! 😀