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n03@n’s Machine Learning notes

00 Prerequisite Mathematics

01 Summation Notation
02 Product Notation
03 Function properties
04 Points and Vectors
05 Planes
06 Probability Density Functions
07 Univariate Gaussians
08 Differential Calculus
09 Gradients
10 Matrices and Vectors
Introduction to ML Part 1
Introduction to ML Part 2

00 Self Exploration

The Cosine Law and Similarity Measures
Machine Learning concepts
Linear Algebra in Machine Learning Problem space

Scientific Python

Introduction to scientific computing with Python
Introduction to Python programming
Numpy
SciPy
matplotlib
SymPy - Symbolic algebra in Python

CS231N Python

Standford CS231n Basic Python

01 Linear Classifiers and Generalizations

Classifiers
Perceptron
Loss Function
Regularization
Gradient Descent

Project - Automatic Review Analyzer

Automatic Review Analyzer

02 Non-linear Classifiers, Linear regression and Collaborative filtering

Linear Regression
Nonlinear Classification
Principal Component Analysis (PCA)

Project - Digit Recognition

Digital Recognition (MNIST)
Closed Form Solution of Linear Regression
Support Vector Machine
Softmax and Cross Entrophy Loss

03 Neural Networks

Embeddings
The Math Behind Neural Networks
Feed Forward Neural Network
Stochastic Gradient Descent

05 Reinforcement Learning

Reinforcement Learning

Repository
Open issue

Index

By n03@n

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