Solutions to the exercises in Dive into Deep Learning, in PyTorch

Work largely in progress

If you think a solution is incorrect, please open an issue.

• 2: Preliminaries
  • 2.1: Data Manipulation
  • 2.2: Data Preprocessing
  • 2.3: Linear Algebra
  • 2.4: Calculus
  • 2.5: Automatic Differentiation
  • 2.6: Probability
• 3: Linear Neural Networks
  • 3.1: Linear Regression
  • 3.2: Linear Regression Implementation from Scratch
  • 3.3: Concise Implementation of Linear Regression
  • 3.4: Softmax Regression
  • 3.5: The Image Classification Dataset
  • 3.6: Implementation of Softmax Regression from Scratch
  • 3.7: Concise Implementation of Softmax Regression
• 4: Multilayer Perceptrons
  • 4.1: Multilayer Perceptrons
  • 4.2: Implementation of Multilayer Perceptrons from Scratch
  • 4.3: Concise Implementation of Multilayer Perceptrons
  • 4.4: Model Selection, Underfitting, and Overfitting
  • 4.5: Weight Decay
  • 4.6: Dropout
  • 4.7: Forward Propagation, Backward Propagation, and Computational Graphs
  • 4.8: Numerical Stability and Initialization
  • 4.9: Environment and Distribution Shift
  • 4.10: Predicting House Prices on Kaggle
• 5: Deep Learning Computation
• 6: Convolutional Neural Networks
• 7: Modern Convolutional Neural Networks
• 8: Recurrent Neural Networks
• 9: Modern Recurrent Neural Networks
• 10: Attention Mechanisms
• 11: Optimization Algorithms
• 12: Computational Performance
• 13: Computer Vision
• 14: Natural Language Processing: Pretraining
• 15: Natural Language Processing: Applications
• 16: Recommender Systems
• 17: Generative Adversarial Networks