Deep Learning Specialization

Aim

Program Objectives

Program Structure

• Module 1: Introduction to Deep Learning
  1. Overview of Deep Learning
     • Definition and Scope
  2. History and Evolution of Deep Learning
     • Milestones and Key Figures
  3. Key Applications of Deep Learning
     • Real-World Use Cases
  4. Basic Concepts and Terminology
     • Fundamental Terms and Definitions

  Module 2: Neural Networks and Deep Learning

  1. Introduction to Neural Networks
     • Basic Structure and Function
  2. Perceptrons and Multilayer Perceptrons
     • Single-Layer vs. Multi-Layer Perceptrons
  3. Activation Functions
     • Common Activation Functions and Their Roles
  4. Training Neural Networks
     • Process and Techniques
  5. Backpropagation Algorithm
     • Detailed Explanation
  6. Loss Functions and Optimization
     • Types and Applications

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

  1. Hyperparameter Tuning
     • Methods and Strategies
  2. Regularization Techniques
     • L1 and L2 Regularization
     • Dropout
     • Data Augmentation
  3. Optimization Algorithms
     • Gradient Descent Variants
     • Adam, RMSprop, and Other Optimizers
  4. Batch Normalization
     • Concepts and Benefits
  5. Early Stopping and Model Checkpointing
     • Implementation and Advantages

  Module 4: Structuring Machine Learning Projects

  1. Project Workflow and Best Practices
     • End-to-End Process
  2. Data Preparation and Preprocessing
     • Techniques and Tools
  3. Training, Validation, and Test Sets
     • Splitting and Management
  4. Model Selection and Evaluation Metrics
     • Criteria and Methods
  5. Debugging and Error Analysis
     • Strategies and Techniques
  6. Deployment and Monitoring
     • Best Practices and Tools

  Module 5: Convolutional Neural Networks (CNNs)

  1. Introduction to CNNs
     • Basic Concepts and Architecture
  2. Convolutional Layers
     • Function and Implementation
  3. Pooling Layers
     • Types and Applications
  4. Fully Connected Layers
     • Role in CNNs
  5. Transfer Learning and Pre-trained Models
     • Techniques and Benefits
  6. Advanced CNN Architectures
     • AlexNet
     • VGGNet
     • ResNet
     • InceptionNet

  Module 6: Sequence Models

  1. Introduction to Sequence Models
     • Overview and Applications
  2. Recurrent Neural Networks (RNNs)
     • Basic Concepts and Uses
  3. Long Short-Term Memory (LSTM) Networks
     • Structure and Function
  4. Gated Recurrent Units (GRUs)
     • Comparison with LSTMs
  5. Sequence to Sequence Models
     • Applications and Examples
  6. Attention Mechanisms
     • Theory and Implementation
  7. Transformer Models
     • Detailed Overview

  Module 7: Advanced Topics in Deep Learning

  1. Generative Adversarial Networks (GANs)
     • Concepts and Applications
  2. Autoencoders and Variational Autoencoders (VAEs)
     • Theory and Use Cases
  3. Reinforcement Learning
     • Basics and Applications
  4. Deep Reinforcement Learning
     • Advanced Techniques
  5. Meta-Learning and Few-Shot Learning
     • Concepts and Examples
  6. Neural Architecture Search (NAS)
     • Methods and Benefits
  7. Explainable AI and Interpretability
     • Importance and Techniques

  Module 8: Practical Implementations and Case Studies

  1. Image Classification
     • Methods and Applications
  2. Object Detection and Segmentation
     • Techniques and Tools
  3. Natural Language Processing (NLP) Applications
     • Key Applications and Models
  4. Speech Recognition
     • Techniques and Use Cases
  5. Time Series Forecasting
     • Methods and Applications
  6. Recommender Systems
     • Theory and Implementation
  7. Case Studies from Industry
     • Real-World Examples

Who Should Enrol?

Program Outcomes

Fee Structure

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