Machine Learning in C (Episode 1)

Описание: Chapters:
00:00:00 - Intro
00:01:21 - What is Machine Learning
00:03:03 - Mathematical Modeling
00:08:15 - Plan for Today
00:10:32 - Our First Model
00:12:24 - Training Data for the Model
00:17:05 - Initializing the Model
00:19:52 - Measuring How Well Model Works
00:27:56 - Improving the Cost Function
00:32:27 - Approximating Derivatives
00:41:25 - Training Process
00:45:59 - Artifician Neurons
00:50:11 - Adding Bias to the Model
00:56:16 - More Complex Model
00:58:41 - Simple Logic Gates Model
01:06:04 - Activation Function
01:15:24 - Troubleshooting the Model
01:25:04 - Adding Bias to the Gates Model
01:27:36 - Plotting the Cost Function
01:29:28 - Muxiphobia
01:31:43 - How I Understand Bias
01:33:20 - Other Logic Gates
01:36:13 - XOR-gate with 1 neuron
01:38:46 - XOR-gate with multiple neurons
01:49:14 - Coding XOR-gate model
01:57:53 - Human Brain VS Artificial Neural Network
02:00:26 - Continue coding XOR-gate model
02:15:14 - Non-XOR-gates with XOR Architecture
02:18:30 - Looking Inside of Neural Network
02:24:57 - Arbitrary Logic Circuits
02:27:23 - Shapes Classifier
02:29:42 - Better Representation of Neural Networks
02:30:36 - Outro
02:30:50 - Smooch

References:
https://github.com/tsoding/perceptron
Notes: https://github.com/tsoding/ml-notes

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