Coding Challenge #104: Linear Regression with TensorFlow.js

References:

Videos:

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Timestamps:
0:00 Introduction
1:21 What is linear regression?
3:16 What do we need?
3:41 Loss function
4:48 Optimizer
5:26 Formula for line
8:02 Let's Code!
8:23 Add data set -- mouse clicks
9:16 Remap x, y to 0, 1
12:12 Randomly initialize m and b
13:39 predict()
16:13 Optimizer & learning rate
17:25 Loss function
19:24 Minimize loss
22:53 What is the optimizer doing?
27:42 Visualize regression results
32.49 dataSync()
35:00 Adjust learning rate
35:35 Memory management
41:01 Improvements

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

#tensorflowjs #linearregression #stochasticgradientdescent #p5js #tensorflowjs

[israrawan]

At 7:23, line (linear function) represents the prediction. A point belonging to a line must be labelled as "guess", whereas the original label of every example should be labelled as "y". [Slip of 'marker' maybe] Nice video though. 'Desperately' waiting for Layers API video. :)

[cashel]

that 'dan from the future' correction was done in good taste, and was very useful (plus entertaining)

very very good way of making sure you cover topics properly :)

[MatthewBishop]

This is a million times better than commercial courses like Udacity's AI "nanodegree" where they drive around in a self-driving car trying to show you how amazing and successful they are.

[israrawan]

There is this pretty cool 'syntax' to define a variable in tensorflow.js. Instead of wrapping the tf.scalar around the tf.variable (13:00), we can actually use chaining. For example: tf.variable(tf.scalar(random (1))) is same as [As I am reading documentation while watching this video, I thought I should share this "syntactic sugar"]

[muhammedshameel]

I love this channel because it has time travels. love it when he does that

[terjeoseberg]

SGD changes m and b in small steps in the direction of the gradient. The step size is determined by the learning rate. It’s not random, but the direction of the gradient can change as it learns, so it needs to be recalculated after each time m and b are updated.

[MajorMandyKitten]

Your videos keep getting better. I generally use c++ and c#, but your videos are good for finding fun ideas and useful ideas. Keep it up, man!

[caiovitullo]

I've done many ML courses, but yours way to explain is being the best! Congratiulations!!!

[braidata]

Antes de ver el video, el solo hecho de que sea de Daniel, me hace sentir que por fin desbloquearé este tema del todo y en javascript. Gracias Daniel, en mi caso el mejor profesor!

[Osmosphere]

Wow - thank you so much! Finally found an great introduction to TensorFlow.js and machine learning that I understand and makes fun! I added the loss value as additional line to the graph :-)

[HamzaAli-ncfx]

I love you sir. You have my respects. I never took interest in machine learning but after watching the previous videos and this one, I am surely gonna dive more into it. Thank you again.

[RingoDemichet]

Hooray ! A coding challenge with tensorflow !

[carllee]

finally get the real example with tensorflow js
coool

[fluencygod]

I first noticed that a tensor flow was possible with js.It's very very amazing!!

[acorad]

omg didn't notice it lasted 44 minutes! It was a blast, good video!

[edujbb]

Thanks for your video tutorial, they are great.

[applebanana]

Excellent! Now we're coding with Power!

[ramseshendriks]

2:25 in fact that is not a linear relation in reality.
because mass grows with volume and volume grows with the third power of lenght

[eltitanthanos]

Pro, your ability to coding is amazing, you must let the virtual come out to reality, you should program arduino and create a incredible project genetic algorithm neuronal net evolution robot

[blasttrash]

the future dan was awesome lol. i felt like i was really watching a movie. :P