PyTorch

Learn the basics of PyTorch and its ecosystem.

Set up PyTorch in local and cloud environments with GPU/TPU support, IDE integration, and dependency management.

Learn about tensors, operations, and PyTorch’s automatic differentiation (autograd) system for gradient computation.

Explore PyTorch modules for creating, training, and evaluating deep learning models with ease.

Master training loops, optimization strategies, and hyperparameter tuning for building efficient models.

Implement convolutional neural networks (CNNs) for image classification, detection, and transfer learning.

Build NLP applications using embeddings, RNNs, LSTMs, GRUs, and transformers with PyTorch.

Use PyTorch Lightning to simplify model training, reduce boilerplate, and improve reproducibility.

Deploy PyTorch models in production environments, including mobile, web, and cloud platforms.

Learn techniques for scaling PyTorch models across GPUs and clusters while optimizing performance.