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