Deep Learning Fundamentals: Code Materials and Exercises

This repository contains code materials & exercises for Deep Learning Fundamentals course by Sebastian Raschka and Lightning AI.

• Link to the course website: https://lightning.ai/pages/courses/deep-learning-fundamentals/
• Link to the discussion forum: https://github.com/Lightning-AI/dl-fundamentals/discussions
• Reach out to Lightning & Sebastian on social media: @LightningAI @rasbt

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For other announcements, updates, and additional materials, you can follow Lightning AI and Sebastian on Twitter!

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Links to the materials

Unit 1. Welcome to Machine Learning and Deep Learning

• 1.6 Implementing a Perceptron in Python
• Unit 1 exercises
  • Exercise 1: Add early-stopping to make the Perceptron more efficient
  • Exercise 2: Initialize the model parameters with small random numbers instead of 0's
  • Exercise 3: Use a learning rate for updating the weights and bias unit

Unit 2. First Steps with PyTorch: Using Tensors

• 2.2 What Are Tensors?
• 2.3 How Do We Use Tensors in PyTorch?
• 2.4 Improving Code Efficiency with Linear Algebra
• 2.5 Debugging Code
• 2.6 Revisiting the Perceptron Algorithm
• Unit 2 exercises
  • Exercise 1: Introducing more PyTorch functions to make your code more efficient
  • Exercise 2: Make the perceptron more efficient using matrix multiplication

Unit 3. Model Training in PyTorch

• 3.6 Training a Logistic Regression Model in PyTorch
• Unit 3 exercises
  • Exercise 1: Banknote Authentication
  • Exercise 2: Standardization

Unit 4. Training Multilayer Neural Networks

• 4.3 Training a Multilayer Perceptron in PyTorch
  • XOR data
  • MNIST data
• 4.4 Defining Efficient Data Loaders
• 4.5 Multilayer Neural Networks for Regression
• Unit 4 exercises
  • TBA

Unit 5. Coming Soon

Unit 6. Coming Soon

Unit 7. Coming Soon

Unit 8. Coming Soon

Unit 9. Coming Soon

Unit 10. Coming Soon