Deep Learning Specialization

Module 1: Introduction to Deep Learning

• Overview of Deep Learning Definition and Scope
• History and Evolution of Deep Learning Milestones and Key Figures
• Key Applications of Deep Learning Real-World Use Cases
• Basic Concepts and Terminology Fundamental Terms and Definitions

Module 2: Neural Networks and Deep Learning

• Introduction to Neural Networks Basic Structure and Function
• Perceptrons and Multilayer Perceptrons Single-Layer vs. Multi-Layer Perceptrons
• Activation Functions Common Activation Functions and Their Roles
• Training Neural Networks Process and Techniques

Module 3: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization

• Hyperparameter Tuning Methods and Strategies
• Regularization Techniques L1 and L2 Regularization
• Dropout
• Data Augmentation

Module 4: Structuring Machine Learning Projects

• Project Workflow and Best Practices End-to-End Process
• Data Preparation and Preprocessing Techniques and Tools
• Training, Validation, and Test Sets Splitting and Management
• Model Selection and Evaluation Metrics Criteria and Methods

Module 5: Convolutional Neural Networks (CNNs)

• Introduction to CNNs Basic Concepts and Architecture
• Convolutional Layers Function and Implementation
• Pooling Layers Types and Applications
• Fully Connected Layers Role in CNNs

Module 6: Sequence Models

• Introduction to Sequence Models Overview and Applications
• Recurrent Neural Networks (RNNs) Basic Concepts and Uses
• Long Short-Term Memory (LSTM) Networks Structure and Function
• Gated Recurrent Units (GRUs) Comparison with LSTMs

Module 7: Advanced Topics in Deep Learning

• Generative Adversarial Networks (GANs) Concepts and Applications
• Autoencoders and Variational Autoencoders (VAEs) Theory and Use Cases
• Reinforcement Learning Basics and Applications
• Deep Reinforcement Learning Advanced Techniques