Mastering PyTorch - 100 Days: 100 Projects Bootcamp Training

Explore tensors in PyTorch, like arrays in NumPy, including rank, shape, data type, and creation methods from lists or NumPy; master tensor operations like indexing, slicing, broadcasting, and in-place updates.

Leverage PyTorch autograd to automatically compute gradients via dynamic computation graphs for backpropagation, with higher-order gradient support and flexible model building.

Build simple neural networks in PyTorch by defining neurons and layers, performing forward passes, and training with loss functions and optimizers like SGD and Adam using the torch.nn module.

Load and preprocess data for neural networks using PyTorch and torchvision datasets. Implement normalization, resizing, and transformations with custom datasets, data loaders, and batching.

Learn to evaluate and validate PyTorch models using accuracy, precision, recall, and F1, with train/validation split, k-fold cross-validation, monitoring, regularization, dropout, and early stopping.

Master convolutional neural networks for image data, including LeNet, AlexNet, VGG, and ResNet with filters, stride, padding, and pooling, and cover RNNs, LSTMs, and transformer self-attention for NLP and translation.

Leverage pre-trained models from Torchvision and Hugging Face transformers to accelerate training via transfer learning, then apply feature extraction or fine-tuning with domain-specific datasets, managing hyperparameters to avoid overfitting.

Learn to handle complex data types, including images, text, and time series, through image augmentation, text preprocessing and tokenization, and time series feature engineering for robust deep learning models.

Learn to save, load, and serialize PyTorch models with TorchScript and ONNX, deploy via Flask, FastAPI, or AWS Lambda, and implement monitoring and versioning in production.

Master debugging and troubleshooting in PyTorch by identifying errors and warnings, ensuring numerical stability against Nans and infinities, and applying profiling and optimization techniques such as batching and mixed precision.

Master distributed training with PyTorch's distributed data parallel, optimize performance through gradient accumulation, mixed precision, and memory management, and scale across multiple GPUs or nodes.

Design and implement custom neural network layers and loss functions with torch.nn, including advanced activations like swish, mish, and gelu, and apply dropout and weight decay for regularization.

Mastering PyTorch teaches reproducibility, experiment tracking with Neptune and weights and biases, and hyperparameter tuning (grid search, random search, Bayesian optimization), plus staying current with arXiv and Google Scholar.

Explore how to integrate PyTorch with other libraries such as TensorFlow, Keras, Onnx, OpenCV, and NLP tools like Spacy and NLTK, enabling flexible, multi-framework models and workflows.

Contribute to PyTorch by following contribution guidelines, forking and branching, coding and testing, and submitting PRs while engaging with the community through forums, mailing lists, and social media.