Applied Math
Linear Algebra
Probability and Information Theory
Numerical Computation
Machine Learning Basis
Math
Probabilities
Standard Distribution
Foundational Ideas
Overview of Machine Learning
Essential Statistics
Measuring Performance
Bayes’ Rule
Curves and Surfaces
Information Theory
Foundational Concepts
Gradient Descent
Structured Distributions
Transformers
Graph Neural Networks
Sampling
Discrete Latent Variables
Continuous Latent Variables.
Generative Adversarial Networks
Normalizing Flows
Autoencoders
Diffusion Models
Basics of Deep Learning
Neural Networks
Backpropagation
Optimizers
Advanced Concepts
Convolutional neural network
Convnets in practice
Autoencoders
Recurrent neural networks
Attention and transformers
Reinforcement learning
Generative adversarial neural networks
Creative Applications of DL
Modern Practices
Deep feedforward networks
Regularization for deep learning
Optimization for training deep models.
Convolutional Networks
Sequence Modeling : Recurrent and Recursive Nets
Research
Linear Factor Models
Autoencoders
Representation Learning
Structured Probabilistic Models for Deep Learning
Monte Carlo Learning
Confronting the partition function
Approximate inference
Deep Generative Models.