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
Support:
- BTC: bc1qj820dmeazpeq5pjn89mlh9lhws7ghs9v34x9v9