PyTorch for Data Scientists: Tensors, Neural Networks, and Transfer Learning

Empower your data science skillset with PyTorch: master tensors, build neural networks, and leverage transfer learning for real-world applications.

...

Introduction to PyTorch and Tensors

Unit 1: PyTorch Fundamentals

Unit 2: Tensor Manipulation

Unit 3: Advanced Tensor Operations

Building Neural Networks with `nn.Module`

Unit 1: Neural Network Fundamentals

Unit 2: Introduction to `nn.Module`

Unit 3: Connecting the Dots: Building More Complex Networks

Automatic Differentiation with `torch.autograd`

Unit 1: Understanding Automatic Differentiation

Unit 2: Backpropagation and Gradient Descent

Unit 3: Advanced Autograd Techniques

Optimization with `torch.optim`

Unit 1: Introduction to Optimization Algorithms

Unit 2: Popular Optimization Algorithms

Unit 3: Learning Rates and Schedules

Data Loading and Preprocessing with `torchvision` and `DataLoader`

Unit 1: Loading Data with `torchvision.datasets`

Unit 2: Transforming Data with `torchvision.transforms`

Unit 3: Efficient Data Loading with `DataLoader`

Unit 4: Creating Custom Datasets

Transfer Learning with Pre-trained Models

Unit 1: Understanding Transfer Learning

Unit 2: Loading and Preparing Pre-trained Models

Unit 3: Fine-tuning Pre-trained Models

Unit 4: Evaluating Transfer Learning Performance

Unit 5: Advanced Transfer Learning Techniques

Training Best Practices and Regularization

Unit 1: Overfitting and Monitoring Training

Unit 2: Regularization Techniques

Unit 3: Early Stopping and Data Preprocessing

Advanced Techniques and Model Deployment

Unit 1: Convolutional Neural Networks (CNNs)

Unit 2: Recurrent Neural Networks (RNNs)

Unit 3: Improving Model Performance

Unit 4: Model Deployment