Introduction to Neural Networks and PyTorch Course Syllabus

This course provides a hands-on introduction to deep learning using PyTorch, designed for learners with prior experience in Python and machine learning. Over six modules, you'll gain practical skills in building and training neural networks, from foundational concepts to deploying trained models. Each module combines theoretical understanding with coding exercises, culminating in a final project. The course requires approximately 40–50 hours to complete, with a recommended pace of one module per week, making it ideal for intermediate learners aiming to strengthen their applied deep learning expertise.

Module 1: Introduction to Deep Learning and PyTorch

Estimated time: 6 hours

• Overview of neural networks and deep learning
• Setting up PyTorch environment
• Introduction to tensors and tensor operations
• Basics of PyTorch syntax and workflow

Module 2: Building Neural Networks with PyTorch

Estimated time: 8 hours

• Understanding model architecture in PyTorch
• Implementing forward and backward passes
• Training neural networks from scratch
• Hands-on: Building a feedforward neural network

Module 3: Activation and Loss Functions

Estimated time: 8 hours

• Common activation functions: Sigmoid, ReLU, Tanh
• Loss functions: Cross-entropy and Mean Squared Error (MSE)
• Selecting appropriate functions for tasks
• Experimenting with function combinations in PyTorch

Module 4: Optimization and Backpropagation

Estimated time: 8 hours

• Gradient descent and backpropagation mechanics
• Implementing backpropagation in PyTorch
• Using optimizers: SGD and Adam
• Tuning optimization for model performance

Module 5: Convolutional Neural Networks (CNNs)

Estimated time: 10 hours

• Convolutional layers and feature extraction
• Pooling layers and CNN architecture
• Building CNNs for image classification
• Hands-on: Training a CNN with PyTorch

Module 6: Final Project

Estimated time: 10 hours

• Design and train a complete deep learning model
• Evaluate model performance using metrics
• Save and serialize the trained model for deployment

Prerequisites

• Familiarity with Python programming
• Basic understanding of machine learning concepts
• Experience with linear algebra and calculus fundamentals

What You'll Be Able to Do After

• Understand the architecture and operation of deep neural networks
• Build and train deep learning models using PyTorch
• Apply activation functions, loss functions, and optimizers effectively
• Use convolutional neural networks for image classification tasks
• Evaluate and deploy trained deep learning models