100+ PyTorch Coding Exercises for Deep Learning

Learn the fundamentals of PyTorch, including tensors and the autograd system, through six coding exercises on installation, broadcasting, reshaping, and indexing.

Develop practical PyTorch skills through six coding exercises: install and verify PyTorch, create and operate on tensors, apply broadcasting and reshaping, use autograd for gradients, and index, slice, and concatenate.

Learn PyTorch for linear algebra by mastering matrix multiplication, solving linear equations, and working with SVD and sparse matrices, with practical applications in PyTorch.

Practice PyTorch coding through hands-on exercises in matrix multiplication, addition, and transposition; solve linear systems, compute singular value decomposition, work with sparse matrices, and tackle least-squares problems.

Learn to build neural networks from scratch with PyTorch, implementing forward and backward propagation, activation and loss functions, and training a simple classifier through hands-on coding exercises.

Develop PyTorch neural networks through exercises, building a model with a single hidden layer, implementing forward and backward propagation, applying activation, loss, and optimization techniques for classification on synthetic data.

Explore deep learning techniques with PyTorch, building and training neural networks including CNNs, using forward and backward propagation with activation, loss, and optimization for real-world classification tasks, plus coding exercises.

Explore transfer learning with pre-trained models in PyTorch, load a ResNet, modify the final layer, fine-tune on new data, and apply data augmentation techniques.

Explore working with data in PyTorch through articles and six coding exercises and solutions, covering custom datasets, data loaders, data augmentation, preprocessing, transformation, and efficient batch processing for deep learning.

Develop six PyTorch coding exercises, including building a custom dataset with a data loader, applying data augmentation and pre-processing, and batching with a training loop for CIFAR-10 and MNIST.

Master PyTorch optimization techniques in Python by implementing SGD and Adam, tuning learning rates with scheduling, applying momentum and weight decay, using early stopping and model checkpointing, and comparing optimizers.

Explore six PyTorch coding exercises that compare optimizers such as SGD and Adam, apply learning rate scheduling, momentum, weight decay, early stopping with checkpointing, and grid search.

Explore advanced neural network architectures in Python PyTorch through six coding exercises, including RNNs, LSTMs, GANs, and autoencoders for sequence prediction, time series analysis, and unsupervised learning.

Explore PyTorch coding exercises on RNNs, LSTMs, sequence forecasting, GANs, and autoencoders. Learn to import libraries, build models, prepare data, train, and evaluate using accuracy, precision, recall, and F1 score.

Explore customizing models with PyTorch through coding exercises that cover creating custom layers and modules, custom losses, hooks, optimizers, learning rate schedules, and building complex models for practical applications.

Practice coding exercises to implement a custom neural network layer, create a custom loss, use training hooks, and build with a custom optimizer and scheduler for real-world classification tasks.