Learn the fundamentals of PyTorch for building and training deep learning models. This course covers tensors, automatic differentiation, neural network modules, data loading, and training procedures. Ideal for developers and engineers with basic Python and ML knowledge.
Prerequisites Basic Python & ML knowledge
Level:
Tensor Manipulation
Create, manipulate, and perform operations on PyTorch Tensors.
Automatic Differentiation
Understand and apply PyTorch's Autograd system for gradient computation.
Neural Network Construction
Build neural networks using torch.nn modules, layers, and activation functions.
Data Loading
Prepare and load data efficiently using PyTorch Datasets and DataLoaders.
Model Training
Implement complete training and evaluation loops for deep learning models.
Model Persistence
Save and load trained PyTorch models and checkpoints.
Basic Architectures
Construct simple Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
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