How To Build a Neural Network from Scratch with Python

In this video, Yuri Gorokhov discusses the fundamental concepts of neural networks and shows us how to build a neural network from scratch using Python.

Chapters:
0:00 Intro
2:05 The theory behind neural networks
5:53 Defining learning for neural networks
13:30 Modeling the math behind learning
17:02 Quantifying the veracity of the belief
19:45 Updating the belief after the error
24:50 Minimizing the error function with derivatives
32:04 Solving for the minimum
36:44 Creating a neural network
39:17 Activation functions
43:05 Defining the “x” in a Relu function
45:47 Getting into the code
50:45 Helper functions and learning classes
54:17 Increasing complexity
58:37 Adding a sine and inverse function
1:03:06 The network size affect the learning rate
1:06:41 The practical application of this code
1:10:54 How to learn more about neural networks
1:16:11 Has machine learning reached a plateau?
1:20:40 Wrap up

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