Practical Deep Learning with PyTorch Course Syllabus

Overview: This beginner-friendly course provides a hands-on introduction to deep learning using PyTorch, designed to take you from foundational concepts to building and evaluating real-world models. The curriculum spans approximately 6 hours of content, structured into six comprehensive modules that blend theory with practical implementation. You'll work with real datasets like MNIST and CIFAR-10, gain experience in building neural networks and convolutional neural networks, and learn essential techniques for training, evaluation, and optimization. By the end, you’ll complete an end-to-end project that solidifies your skills and prepares you for real-world applications.

Module 1: Introduction to Deep Learning & PyTorch

Estimated time: 0.5 hours

• Core principles of deep learning
• Understanding PyTorch's role in deep learning
• Setting up the development environment
• Working with tensors in PyTorch

Module 2: Building Neural Networks

Estimated time: 1 hour

• Structure of neural networks: layers and connections
• Activation functions and their implementation
• Defining loss functions and optimizers
• Creating and training your first neural network in PyTorch

Module 3: Training & Evaluation Techniques

Estimated time: 0.75 hours

• Data preprocessing for model input
• Implementing data batching
• Constructing training loops
• Tracking loss and accuracy during training

Module 4: Convolutional Neural Networks (CNNs)

Estimated time: 1 hour

• Understanding CNN architecture and components
• Use cases for CNNs in image processing
• Implementing a CNN for image classification

Module 5: Avoiding Overfitting & Model Optimization

Estimated time: 0.75 hours

• Applying dropout and regularization techniques
• Using data augmentation to improve generalization
• Hyperparameter tuning and model checkpointing

Module 6: Real-World Projects with PyTorch

Estimated time: 1.5 hours

• Working with real-world datasets (MNIST, CIFAR-10)
• Building an end-to-end image classification pipeline
• Evaluating model performance and making improvements

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Access to a computer with internet for installing PyTorch

What You'll Be Able to Do After

• Understand deep learning fundamentals and implement them using PyTorch
• Build and train neural networks from scratch
• Master convolutional neural networks for image classification tasks
• Apply techniques to prevent overfitting and optimize model performance
• Gain hands-on experience with real-world datasets and model evaluation