Deep Learning Fundamentals: Code Materials and Exercises
This repository contains code materials & exercises for Deep Learning Fundamentals course by Sebastian Raschka and Lightning AI.
For other announcements, updates, and additional materials, you can follow Lightning AI and Sebastian on Twitter!
Links to the materials
Unit 1. Welcome to Machine Learning and Deep Learning [ Link to videos ]
- 1.1 What Is Machine Learning?
- 1.2 How Can We Use Machine Learning?
- 1.3 A Typical Machine Learning Workflow (The Supervised Learning Workflow)
- 1.4 The First Machine Learning Classifier
- 1.5 Setting Up Our Computing Environment
- 1.6 Implementing a Perceptron in Python
- 1.7 Evaluating Machine Learning Models
- Unit 1 exercises
Unit 2. First Steps with PyTorch: Using Tensors [ Link to videos ]
Unit 3. Model Training in PyTorch [ Link to videos ]
- 3.1 Using Logistic Regression for Classification
- 3.2 The Logistic Regression Computation Graph
- 3.3 Model Training with Stochastic Gradient Descent
- 3.4 Automatic Differentiation in PyTorch
- 3.5 The PyTorch API
- 3.6 Training a Logistic Regression Model in PyTorch
- 3.7 Feature Normalization
- Unit 3 exercises
Unit 4. Training Multilayer Neural Networks [ Link to videos ]
Unit 5. Organizing your PyTorch Code with Lightning [ Link to videos ]
Unit 6. Essential Deep Learning Tips & Tricks [ Link to videos ]
Unit 7. Getting Started with Computer Vision [ Link to videos ]
Unit 8. Introduction to Natural Language Processing and Large Language Models [ Link to videos ]
Unit 9. Techniques for Speeding Up Model Training [ Link to videos ]
Unit 10. The Finale: Our Next Steps After AI Model Training [ Link to videos ]