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Self Paced

Deep Learning Specialization

Mastering Deep Learning for Advanced AI Research and Development

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Enroll now for early access of e-LMS

MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Advanced

DURATION
8 Weeks

About

This self-paced specialization provides an in-depth exploration of deep learning, covering theoretical foundations and practical implementations. Participants will gain expertise in neural networks, convolutional networks, sequence models, and other advanced topics, preparing them for cutting-edge AI research and applications.

Aim

To equip PhD scholars and academicians with comprehensive knowledge and practical skills in deep learning techniques and neural networks, essential for advanced AI research and development roles.

Program Objectives

Master deep learning techniques and neural networks.
Apply deep learning models to real-world problems.
Optimize and improve deep learning models.
Conduct advanced AI research.
Implement state-of-the-art deep learning projects.

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

Participant’s Eligibility

AI researchers, data scientists, machine learning engineers, and academic professionals in AI and computer science.

Program Outcomes

Develop and implement deep learning models.
Optimize neural networks for better performance.
Apply deep learning techniques to various domains.
Conduct innovative AI research.
Lead AI projects with advanced deep learning skills.

Fee Structure

Standard Fee: INR ~~10,998~~ USD ~~198~~

Discounted Fee: INR 5499 USD 99

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Certificate

Program Assessment

Certification to this program will be based on the evaluation of following assignment (s)/ examinations:

Exam	Weightage
Mid Term Assignments	50 %
Project Report Submission (Includes Mandatory Paper Publication)	50 %

To study the printed/online course material, submit and clear, the mid term assignments, project work/research study (in completion of project work/research study, a final report must be submitted) and the online examination, you are allotted a 1-month period. You will be awarded a certificate, only after successful completion/ and clearance of all the aforesaid assignment(s) and examinations.