10.2: Neural Networks: Perceptron Part 1 - The Nature of Code

References:

Timestamps:
0:00 Introduction
0:54 What is a perceptron?
3:17 Classify whether a point is above/below a line
5:25 Supervised learning
7:55 Activation functions
10:22 Initializing the weights
11:39 Perceptron class
13:19 Guess function
18:55 Create a dataset
23:10 Supervised learning
25:16 Updating weights
28:33 Training
31:00 Learning rate
38:06 Train one point at a time
41:35 Bias
44:24 Thanks for watching!

Editing by Mathieu Blanchette
Animations by Jason Heglund
Music from Epidemic Sound

#neuralnetworks #perceptron #processing

[paoloricciuti]

One thing you should definitely do is try your Perceptron with new data: you can create another 100 Points, exclude these Points from training and test the Perceptron with these Points...also a very cool way to test you Perceptron is to add a Point at the click of mouse and let it label from the Perceptron (this way if it's wrong you are gonna see a black point inside the white blob). Anyway really good work, i love your way of teach, i love programming, i love machine learning and out of all the videos and blog post and slides that i read you are the one that can really make something easy to understand. Never stop to be like this! ;)

P.s. if you need it i made a porting to p5.js with this suggestions implemented. :)

[chinmaybharti]

your speaking skills, your interactiveness, your teaching ability...best online teacher I've ever met

[ianchui]

18:05 that pen flip was so smooth lmfao

[TheRayll]

I'd recommend this to everyone over any movie

[zendoclone]

love that you focus more on the conceptual side of programming more than the nitty gritty details. It's really annoying to see all these other videos that do something like "we have A then we have B, then voila! life!" I've honestly been waiting for a video like this for a long time.

[Efferto]

"How to train your Perceptron" (2017)
IMDb 8.7/10

[CJKims]

First impression: 44 minutes? Are you crazy? I'm not going to waste my time on a single video!
After done watching: (quietly clicks subscribe button) ....

[joeydash]

who ever you are thank you very much ....I love watching all your video and learn new stuffs

[keyboardbandit]

I can't believe this video was released today! I just started working on a problem at work (internship) that needs a (albeit more complicated) neural network to solve! This was a perfect primer to help me really understand the basics! The fit(), train(), and activate() functions is scikit-learn seem far less magical and way more accessible to me now!

THANK YOU SO MUCH!

[shaileshrana]

Thank you so much, Daniel. I have never studied coding formally. Started with watching your coding challenges and I'm happy to say that this amazing and simple explanation is exactly what I needed to start my journey into machine learning. You've inspired me and taught me. Thank you.

[thisaintmyrealname]

The suspense built by the code and then watching it all work so well was amazing. It was also a very good idea to show the progress with each click.

[micahgilbertcubing]

I love this series so much! Most machine learning / neural network explanations and tutorials are either designed for 5th graders or people with a college degree. The mathematical parts and coding are perfect for a high schools CS student. Thanks so much for finally making me understand backpropogation!

[dalegriffiths]

Also followed along but doing it in javascript. Cool little project. I trained it on 500 circles and then did a test set of 100. I used frameRate instead of mouse click to slow animation. Occasionally got some weird oscillating behaviour near around 470-480 correct on the training data but usually got it quickly. Also once trained with the training data normally got all 100 test data on one epoch but sometimes got stuck at around 98/99 correct (that only happened around 10% of attempts). I watched the self driving car video and that sent me back to flappy birds and now neural nets before doing flappy birds neuroevolution and then finally back to self driving cars. Once i've a handle of this in javascript i want to do it in something like unity using c#, this will need me to learn blender to make my circuit. Much more fun than watching endless hours of Netflix whilst in lockdown! Happy coding all.

[tinkumonikalita]

This is how powerful processing can be in understanding a concept. Great video Dan as always.

[DadanHamdaniTop]

This is the clearest explanation on machine learning that I have ever watch.

[eck]

This is exactly what i was looking for! Explaining what is actually happening behind the scenes on the background of ml5 and tensorflow, how it's working.
Thank you so much for this!

[shankarkarthik]

OMG - I am going through Coursera ML course and your video is simply amazing. I cannot thank you with words for offering these videos for free. Love your fun filled way and going through concepts on NN sessions in one at a time.

[augre-app]

you are the only person on youtube who actually practically shows how this all works. actually coding it.

[prateek]

I have become accustomed to listening long lecture videos at ~2x speed. And watching your videos at 1.75x hits the sweet spot.

[isaiahsias]

Dude, I cannot thank you enough for diving into this concept in such an engaging way. It beats trying to break it down from a completely algebraic standpoint