Learn Reinforcement Learning with PyTorch

Module 1: Foundations—Vectors, Matrices, and PyTorch Basics

1. Introduction: The Course, the Blog, and Why PyTorch
2. Vectors and Scalars—Hands-On with PyTorch Tensors
3. Basic Vector Operations: Addition, Scalar Multiplication, Dot Product
4. Matrices: Construction, Shapes, and Basic Matrix Operations
5. Broadcasting and Elementwise Operations in PyTorch
6. Matrix Multiplication and Transpose—What, Why, and How
7. Geometry with Tensors: Norms, Distance, Angles, Projections
8. Linear Transformations and Simple Data Transformations
9. Hands-On Mini-Project: Visualizing and Transforming Data with Tensors

Module 2: Optimization and Learning—From Gradients to Linear Models

1. Introduction to Gradient Descent—Math and Code
2. Autograd in PyTorch: Automatic Differentiation Demystified
3. Loss Functions and Cost Surfaces—Visual and Practical Intuition
4. Fitting a Line: Linear Regression from Scratch with PyTorch
5. Fitting Nonlinear Curves: Polynomial Regression
6. Classification Basics: Logistic Regression
7. Softmax and Multiclass Classification
8. Mini-Project: Build, Train, and Visualize a Simple Classifier

Module 3: Neural Networks—Building Blocks and Training

1. The Perceptron: Oldest Neural Network
2. Feedforward Neural Networks from Scratch (No nn.Module)
3. Building with torch.nn: The Convenient Way
4. Activation Functions: Sigmoid, Tanh, ReLU, LeakyReLU, etc.
5. Backpropagation: Intuition and Hands-On Example
6. Overfitting, Underfitting, and Regularization
7. Dropout, L2, and Other Regularization in PyTorch
8. Mini-Project: MNIST Digit Classifier (Shallow NN)

Module 4: Introduction to Reinforcement Learning—Concepts & Classic Problems

1. What is Reinforcement Learning? RL vs. Supervised/Unsupervised
2. Markov Decision Processes: States, Actions, Rewards, Policies
3. Bandit Problems: Exploration vs. Exploitation
4. Tabular Value-Based Methods: Q-Learning and SARSA
5. Monte Carlo and Temporal Difference (TD) Learning
6. Policies, Value Functions, and Bellman Equations
7. Mini-Project: RL Agent on Custom Gridworld

Module 5: Deep Reinforcement Learning—From Q-Tables to Deep Networks

1. Limitations of Tabular RL and the Need for Function Approximation
2. Deep Q-Networks (DQN): Concepts and PyTorch Implementation
3. Experience Replay and Target Networks
4. Extensions: Double DQN, Dueling DQN