Machine Learning Exposed: Introduction to Machine Learning
summary
Starts with a hands‑on linear regression example predicting house price using a single feature, shows the squared‑error cost and finds optima with gradient descent and backprop, walks through the 150‑row Iris dataset (train/test split) and an XOR demo with live forward‑prop calculations, demos a browser Angular2 + Java/Spring visualization using DeepLearning4J, explains convolutional nets (filters, pooling) for digit recognition, shows t‑SNE/TensorFlow projector for embeddings, a Magenta MIDI RNN demo and a Tesla self‑driving video.
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chapters (23)
- 0:00Start
- 0:18Machine Learning Exposed
- 1:52Why this Machine Learning
- 4:06Three Categories of Machine Learning
- 5:59Unsupervised Learning
- 6:28Reinforcement Learning
- 7:21Supervised Learning
- 9:06Linear Regression
- 12:18Improve Linear Regression
- 12:30Squared Error
- 14:31Gradient Descent
- 16:32Learning Rate
- 17:53The Iris Data Set
- 19:02Decision Boundaries
- 23:38.Machine Learning Visualization Application
- 26:17Machine Learning Visualization
- 27:52Sigmoid Activation Function
- 31:27Convolutional Neural Network
- 33:10Optimizing Your Neural Network
- 35:17Neural Networks and Linear Regression
- 37:01Euclidean Distance
- 39:12Summary
- 43:26Ai Duet
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