Perceptron Algorithm with Code Example - ML for beginners!

In this video we will talk about the perceptron and code it together! 🧠
Perceptron is single node in an artificial neural network 🤖
It's an an algorithm (a set of instructions) 📃 that's meant to mimic a neuron inside a human brain! We will discuss all the different components of perceptron and in the very end - we'll of course implement what we have learned with Python 🐍 to fully understand the math and logic behind the concepts!
💻 Get the complete perceptron code! 💻
wayscript.com/script/VlHE0uL8
(the above script runs on the cloud - no need to install Python 😉)
🛑 BEFORE YOU WATCH THIS VIDEO 🛑
Watch my Simple Introduction to Artificial Intelligence: 🤖🧠
• Machine Learning FOR B...
🛑 AFTER YOU WATCH THIS VIDEO 🛑
Watch my Error Function - Cross-Entropy Loss Tutorial:
• Cross Entropy Loss Err...
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🧐 TIMESTAMPS 🧐
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00:00 - perceptron definition
00:33 - real life example
01:40 - perceptron example
02:04 - perceptron input
02:15 - perceptron weights
02:45 - weighted sum
03:00 - threshold / bias
03:20 - activation function (step function)
03:58 - key notes
05:04 - python perceptron
08:36 - thanks for watching!
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All the beautiful icons in the video are from:
www.flaticon.com/

[@cxvxcbcxn]

I've watched many Python tutorials on KZbin, 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!

[@felixnyamongo]

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

[@fallinginthed33p]

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.

[@utkarshkapil]

Exactly the kind of explanation I was looking for , THANK YOU!!!!!

[@khafrewatkins]

This made the Perceptron concept much more clear and easy to understand. Not a given on youtube. Thanks Python Simpliefied!

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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!

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Oh boy! At 73 yrs old I'm finally able to begin to understand Neural Networks. Thankyou so much.

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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! 😊

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Love how simple you made it

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Crystal clear Explanation with great code demonstration. Thanks you.

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[@PythonSimplified]

Yeeey!! Thank you so much for the incredible feedback!!! Welcome aboard! (There are plenty of AI tutorials coming in the next few months! 🙂)

[@helloworldcsofficial]

An example with the added bias will be a good next video. Thanks for this one!

[@karunakaranr2473]

Simply the best tutorial. Well done and thanks for your time and help.

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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!)

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Wonderful tuto!

[@AgeOfAsparagus]

This is the perfect level of difficulty for my high school computer science course. I hope you have many more planned!

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simple but very helpful perceptron tutorial. You explained the topic very well!

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Best python chanel i have seen yet, thanks for help !

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I love your simple explanations

[@PythonSimplified]

Yey! Thank you so much! 😀

[@DanteMishima]

Now why wasn't it explained like this to me before? Great video

[@pawelwalenda]

Great lesson as usual. I can't wait for a continuation.

[@PythonSimplified]

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! 😉

[@frankhabermann9083]

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.

[@PythonSimplified]

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! 😁

[@DrMohammadAman]

The way you explained is awesome, the support of python code gives lot of Confidence in understanding the primitive of NN. Thank you

[@darkness3635]

fantastic teaching style....WELL DONE MAAM ....KEEP IT UP..👍👍👍👍

[@dikshyakasaju7541]

Super helpful and fun!

[@rockymountainfacet5958]

Thank you so much for this video. I really enjoyed watching you code a perceptron.

[@message59]

this is so on point 👏🏼 thank you for the effort :)

[@pakinam190]

this really helped! thank you for the great video💖

[@css2014]

Great explanation ! Very easy to understood.

[@TheMSBNA]

I like how you explained it to me the way youd explain to a kid. Thanks!

[@PythonSimplified]

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! 😂

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Cool topic, another awesome video. Great job!

[@PythonSimplified]

Thank you so much Derrick!! I'm super happy you liked it! 😁

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Amazing video my friend!. Very simple and I love it.

[@PythonSimplified]

Thank you so much Dark Wolf, glad you liked it! 😁

[@ms.crawford1335]

So simplified thanks

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Very well explained , Thanks

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Thank you very much. Your explanation is clear and understandable.

[@PythonSimplified]

Thank you Ali, I'm happy you liked my explanation! 😊

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Wooow you are so amazing. Thanks I found what I need.

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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 ;)

[@PythonSimplified]

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!!! 😁

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​@@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

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Thanks for sharing this tutorial

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well done - thanks for sharing.

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Really! wonderful!!!😊

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[@PythonSimplified]

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! 😉

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Informative 😎

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Thanks for sharing. You're amazing!

[@PythonSimplified]

Thank you so much Michael! 😁

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Mariya, your videos are always fun and useful. Do you have any books that you recommend on python machine learning?

[@TheJonathanLugo]

Super explanation. Thanks!!!

[@PythonSimplified]

Thank you so much Jonathan, glad you liked it! :D

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Very Intuitive explanation with visualization. 👍👍👍

[@PythonSimplified]

Thank you so much Pallavi! I'm so happy liked this tutorial! 😁

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Thanks for the video

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Amazing explanattion...really... i am learning a lot with you. You are clear and very pedagogic.. my respects from Argentina

[@PythonSimplified]

Muchas gracias Matias! I'm very happy you like my explanations, trying to simplify everything as much as possible! 😀 Best regards from Vancouver!! 😁🍁

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Thank you so much for the incredible feedback! Super glad to have you onboard! 😀

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Thank you.

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[@PythonSimplified]

Thank you so much! I'm glad you're enjoying my tutorials! 😀

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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. 😋😃

[@PythonSimplified]

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 🤣

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You are a genius

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Thanks. you cover the basic with real world examples .

[@PythonSimplified]

Absolutely! I find it's the best way to learn such complex concepts! 😀

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Thank you ma'am❤

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thank you for the great explanation.

[@PythonSimplified]

Thank you! Happy you liked it! 😃

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Nice tutorial

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Thank you so much Mohamed! 😃

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Thank you dexteuse freeman! 😁

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Nice vídeo, amazing editing.

[@PythonSimplified]

Thank you so much Manuel! 😊

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This was srsly interesting irdk y isn't machine learning being approached this way, simplicity and practicality at the same time

[@PythonSimplified]

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! 😊

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thanks for the video, I will try the ZIP function out and find out how it works.

[@PythonSimplified]

Thank you Tobs, I hope you enjoyed your no chicken no goat dinner! 😀

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[@PythonSimplified]

You're absolutely welcome Joseph, I'm glad you liked it! 😀

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Thank you Nutrioo! 😁

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Thank you so much! Glad to hear that! 😀

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eccelente spiegazione, lineare e semplicegrazie

[@PythonSimplified]

Grazie mille Fabrizio!

[@segunadegunle2560]

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

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Thanks

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Thank you RV Mishra! Glad you liked it! 😀

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Spasibo Oleg! 😁

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Thx

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Really good video on forward propagation. Are you a ML engineer or Data scientist?

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This is very interesting and multi applicable in CNN conventional nerual network.

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Nice

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thanks

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Great

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dis gon be good

[@PythonSimplified]

Thank you Geraldo, I hope you liked it! 😀

[@georgesword2012]

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?

[@LunaMarlowe327]

NICE