Lightning-AI/dl-fundamentals

Lightning-AI/dl-fundamentals

Description: Deep Learning Fundamentals -- Code material and exercises

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Created: 2022-11-09T21:48:20Z

Pushed: 2024-02-28T20:30:26Z

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README:

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 [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
• 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 [Link to videos ]

• 2.1 Introducing PyTorch
• 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
• 2.7 Seeing Predictive Models as Computation Graphs
• 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 [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
• Exercise 1: Banknote Authentication
• Exercise 2: Standardization

Unit 4. Training Multilayer Neural Networks [Link to videos ]

• 4.1 Dealing with More than Two Classes: Softmax Regression
• 4.2 Multilayer Neural Networks and Why We Need Them
• [4.3 Training a Multilayer Perceptron in PyTorch](unit04-multilayer-nets/4.3-mlp-pytorch)
• [XOR data](unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part1-2-xor)
• [MNIST data](unit04-multilayer-nets/4.3-mlp-pytorch/4.3-mlp-pytorch-part3-5-mnist)
• [4.4 Defining Efficient Data Loaders](unit04-multilayer-nets/4.4-dataloaders)
• [4.5 Multilayer Neural Networks for Regression](unit04-multilayer-nets/4.5-mlp-regression)
• 4.6 Speeding Up Model Training Using GPUs
• [Unit 4 exercises](./unit04-multilayer-nets/exercises)
• [Excercise 1: Changing the Number of Layers](./unit04-multilayer-nets/exercises/1_changing-layers)
• [Exercise 2: Implementing a Custom Dataset Class for Fashion MNIST](./unit04-multilayer-nets/exercises/2_fashion-mnist)

Unit 5. Organizing your PyTorch Code with Lightning [Link to videos ]

• 5.1 Organizing Your Code with PyTorch Lightning
• [5.2 Training a Multilayer Perceptron in PyTorch Lightning](./unit05-lightning/5.2-mlp-lightning)
• [5.3 Computing Metrics Efficiently with TorchMetrics](./unit05-lightning/5.3-torchmetrics)
• [5.4 Making Code Reproducible](./unit05-lightning/5.4-reproducibility)
• [5.5 Organizing Your Data Loaders with Data Modules](./unit05-lightning/5.5-datamodules)
• [5.6 The Benefits of Logging Your Model Training](./unit05-lightning/5.6-logging)
• [5.7 Evaluating and Using Models on New Data](./unit05-lightning/5.7-evaluating)
• 5.8 Add functionality with callbacks
• [Unit 5 exercises](./unit05-lightning/exercises)

Unit 6. Essential Deep Learning Tips & Tricks [Link to videos ]

• [6.1 Model...