Machine Learning in C (Episode 1)

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