- Course Roadmap
- Background Materials
- Machine Learning Basics
- Concepts, Capacity, Estimators, Linear Regression
- MLE, Bayesian, Other ML Algorithms
- Stochastic Gradient Descent, etc
- Deep Neural Networks
- Deep Feedforward Neural Networks, Cost, Output, Hidden Units
- Back Propagation
- Keras Sample Codes
- Keras Sample Codes
Tensorflow (tf) Experiments
- Hello World!
- Linear Algebra
- Matrix Decomposition
- Probability Distributions using TensorBoard
- Linear Regression by PseudoInverse
- Linear Regression by Gradient Descent
- Under Fitting in Linear Regression
- Optimal Fitting in Linear Regression
- Over Fitting in Linear Regression
- Nearest Neighbor
- Principal Component Analysis
- Logical Ops by a 2-layer NN (MSE)
- Logical Ops by a 2-layer NN (Cross Entropy)
- NotMNIST Deep Feedforward Network: Code for NN and Code for Pickle
- NotMNIST CNN
- word2vec
- Word Prediction/Story Generation using LSTM. Belling the Cat by Aesop Sample Text Story