PyTorch: Deep Learning and Artificial Intelligence Course Syllabus

This course provides a hands-on introduction to deep learning using PyTorch, designed for beginners with some Python and machine learning background. You'll progress from foundational concepts to building and deploying real-world models through practical coding exercises and projects. The curriculum spans approximately 6.5 hours of video content, with additional time for hands-on implementation and the final project, making it ideal for learners aiming to gain job-ready skills in deep learning.

Module 1: Introduction to Deep Learning & PyTorch

Estimated time: 0.5 hours

• Overview of artificial intelligence and deep learning
• Introduction to the PyTorch framework
• Installing PyTorch and setting up the development environment
• Understanding the role of PyTorch in modern AI development

Module 2: PyTorch Fundamentals

Estimated time: 0.75 hours

• Working with tensors and tensor operations
• Automatic differentiation with autograd
• Key PyTorch libraries and functions
• Building a simple neural network from scratch

Module 3: Neural Network Training Workflow

Estimated time: 1 hour

• Creating data loaders for efficient batch processing
• Selecting and implementing loss functions
• Optimization techniques using gradient descent
• Implementing training loops, validation, and evaluation metrics

Module 4: Image Classification Projects

Estimated time: 1 hour

• Building a convolutional neural network (CNN) for image classification
• Applying data augmentation to improve model generalization
• Implementing dropout and batch normalization for better performance
• Evaluating model accuracy on real-world image datasets

Module 5: Tabular Data Modeling

Estimated time: 1 hour

• Preprocessing structured data for deep learning
• Building dense neural networks for classification and regression
• Training models on tabular datasets using PyTorch

Module 6: Transfer Learning with Pre-trained Models

Estimated time: 1 hour

• Understanding transfer learning concepts
• Using pre-trained models like ResNet and VGG
• Feature extraction and fine-tuning in PyTorch
• Adapting models to new classification tasks

Module 7: Saving, Loading & Deployment

Estimated time: 0.75 hours

• Saving trained models using TorchScript
• Loading models for inference
• Deploying models via simple APIs

Module 8: Final Project: Build an End-to-End Deep Learning App

Estimated time: 1.25 hours

• Combining all learned concepts into a complete application
• Training and evaluating a custom deep learning model
• Deploying the model for real-world use

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• High school level mathematics (linear algebra, calculus basics)

What You'll Be Able to Do After

• Understand the foundations of deep learning and neural networks
• Master PyTorch for building, training, and evaluating models
• Work with real-world datasets for image and tabular data classification
• Implement advanced techniques like transfer learning and custom CNNs
• Build and deploy end-to-end deep learning applications