A deep understanding of deep learning (with Python intro) Course Syllabus

Overview: This course provides a comprehensive, theory-rich introduction to deep learning, combining mathematical foundations with hands-on coding in Python and PyTorch. Designed for beginners and intermediate learners, it emphasizes understanding why models work, not just how to build them. The curriculum spans core concepts, neural network architectures, optimization techniques, and practical implementation using Google Colab with free GPU support. With approximately 30–40 hours of content, including an 8+ hour Python appendix, learners gain deep conceptual insight alongside coding proficiency—no prior Python experience required. Modules blend theory, visualization, and code to build and analyze models from scratch.

Module 1: Deep Learning Fundamentals & Math Theory

Estimated time: 11 hours

• Core calculus and gradient descent
• Loss functions and backpropagation
• Activation functions and network architectures
• Weight initialization and regularization techniques
• Understanding training dynamics and parameter effects

Module 2: Building Neural Architectures in PyTorch

Estimated time: 9 hours

• Implementing neural networks using PyTorch
• Building and training feedforward networks
• Designing convolutional neural networks (CNNs)
• Creating recurrent networks (RNNs) and generative models
• Introduction to autoencoders and basic GANs

Module 3: Advanced Optimization, Regularization & Practical Performance

Estimated time: 5 hours

• Learning rate scheduling and optimizer selection
• Batch normalization and dropout layers
• Hyperparameter tuning strategies
• Diagnosing and mitigating overfitting
• Evaluating model efficiency and convergence

Module 4: Python Refresher & Supporting Tools

Estimated time: 8 hours

• Python basics: variables, loops, functions
• Data structures and NumPy for deep learning
• Data visualization with matplotlib
• Using Google Colab with GPU acceleration
• Hands-on coding exercises for beginners

Module 5: Transfer Learning and Model Analysis

Estimated time: 4 hours

• Applying transfer learning in practice
• Feature extraction using pretrained models
• Visualizing learned filters and model internals
• Generating and interpreting model outputs
• Understanding model failure modes

Module 6: Final Project

Estimated time: 3 hours

• Design and train a custom neural network
• Apply regularization and optimization techniques
• Document model choices and performance analysis

Prerequisites

• No prior coding or deep learning experience required
• Basic familiarity with math (algebra, functions) helpful
• Access to a modern web browser for Colab notebooks

What You'll Be Able to Do After

• Explain the mathematical foundations of deep learning
• Build and train neural networks using PyTorch
• Implement CNNs, RNNs, autoencoders, and GANs from scratch
• Optimize models using dropout, batch norm, and hyperparameter tuning
• Analyze and interpret model behavior and training dynamics