Deep Learning with PyTorch Step-by-Step: Part I – Fundamentals Course Syllabus

Overview: This course provides a hands-on introduction to deep learning using PyTorch, guiding learners from foundational concepts to building, training, and deploying neural networks. With a balanced mix of theory and coding practice, the curriculum spans eight modules, each designed to be completed in about a week, requiring roughly 6-8 hours per module. You'll work with real datasets, implement core deep learning models, and finish with a capstone project that solidifies your skills in a real-world context. Lifetime access ensures you can learn at your own pace and revisit concepts as needed.

Module 1: Introduction to PyTorch & Deep Learning

Estimated time: 7 hours

• Deep learning fundamentals
• PyTorch ecosystem overview
• CPU vs. GPU execution
• Hands-on: Install PyTorch and run a 'Hello, World!' tensor example

Module 2: Tensors, Autograd & Computation Graphs

Estimated time: 7 hours

• Tensor operations and broadcasting
• Gradient tracking with Autograd
• Understanding computational graphs
• Hands-on: Compute gradients for simple functions and implement a manual optimizer

Module 3: Building Neural Networks with nn.Module

Estimated time: 7 hours

• Neural network layers and activation functions
• Model definition using nn.Module
• Forward and backward pass implementation
• Hands-on: Define and train a feedforward network on MNIST classification

Module 4: Training Loop, Loss & Optimization

Estimated time: 7 hours

• Loss functions: CrossEntropy, MSE
• Optimizers: SGD, Adam
• Batching and epochs
• Hands-on: Write a full training and validation loop, plot loss and accuracy curves

Module 5: Convolutional Neural Networks & Transfer Learning

Estimated time: 7 hours

• Convolutional layers and pooling
• Pretrained models and transfer learning
• Fine-tuning strategies
• Hands-on: Build a CNN for CIFAR-10 and fine-tune ResNet on a custom image dataset

Module 6: Recurrent Networks & Sequence Modeling

Estimated time: 7 hours

• RNN, LSTM, and GRU cell architectures
• Sequence-to-sequence modeling basics
• Teacher forcing technique
• Hands-on: Implement a character-level language model and generate text samples

Module 7: Model Deployment & Best Practices

Estimated time: 7 hours

• Saving and loading trained models
• TorchScript and ONNX export
• Reproducibility and performance best practices
• Hands-on: Export a trained model to TorchScript and run inference in a standalone script

Module 8: Capstone Project – End-to-End Deep Learning

Estimated time: 8 hours

• Project scoping and data pipeline setup
• Model selection and evaluation metrics
• Final presentation of results
• Hands-on: Tackle a real-world problem—e.g., image segmentation or sentiment analysis—and present results

Prerequisites

• Familiarity with Python programming
• Basic understanding of linear algebra and calculus
• Introductory knowledge of machine learning concepts

What You'll Be Able to Do After

• Understand the principles of deep learning and PyTorch's role in modern AI
• Manipulate tensors and leverage automatic differentiation for model training
• Build and train custom neural networks using nn.Module and torchvision
• Apply convolutional and recurrent networks to computer vision and NLP tasks
• Deploy trained models using TorchScript and follow best practices for reproducibility