Deep Learning Fundamentals

Master the Core Concepts and Techniques of Deep Learning for Advanced AI Applications

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MODE
Online/ e-LMS

TYPE
Self Paced

LEVEL
Moderate

DURATION
5 Weeks

About

This program introduces the core concepts of deep learning, focusing on neural network architectures, optimization techniques, and common applications. Participants will gain a strong understanding of how to implement and train deep learning models, including hands-on practice using Python and deep learning frameworks like TensorFlow and PyTorch.

Aim

To provide a comprehensive introduction to the foundational concepts of deep learning for PhD scholars, researchers, and data professionals. This course covers key architectures, algorithms, and practical applications of deep learning techniques, enabling participants to build and train neural networks for a variety of complex tasks.

Program Objectives

Understand the fundamental concepts of deep learning.
Build and train deep neural networks using popular frameworks.
Learn key architectures like CNNs and RNNs for specific tasks.
Apply optimization techniques for improving deep learning models.
Gain hands-on experience with real-world deep learning projects.

Program Structure

Introduction to Deep Learning
- Overview of Deep Learning
- Historical Context and Current Trends
- Applications of Deep Learning (e.g., NLP, CV, Autonomous Systems)
Neural Networks Basics
- Neurons, Activation Functions
- Feedforward Networks
- Backpropagation and Gradient Descent
Training Deep Neural Networks
- Loss Functions
- Optimizers (SGD, Adam, etc.)
- Overfitting and Regularization (Dropout, Batch Normalization)
Convolutional Neural Networks (CNNs)
- Introduction to CNNs
- Convolution, Pooling Layers
- Architectures like AlexNet, VGG, ResNet
Recurrent Neural Networks (RNNs)
- Sequence Modeling
- LSTM, GRU, and Attention Mechanisms
- Applications in NLP and Time Series
Deep Learning Frameworks
- Introduction to TensorFlow and PyTorch
- Building Models in TensorFlow/PyTorch
- Customizing Layers and Loss Functions
Autoencoders and Generative Models
- Introduction to Autoencoders
- Variational Autoencoders (VAE)
- Generative Adversarial Networks (GANs)
Advanced Deep Learning Concepts
- Transfer Learning
- Reinforcement Learning Basics
- Transformers and BERT
Model Deployment and Production
- Model Serving
- Model Optimization (Quantization, Pruning)
- Using Models in Real-World Applications (APIs, Cloud, Edge)
Deep Learning Ethics and Fairness
- Bias in AI Models
- Ethical Considerations in AI
- AI for Social Good
Final Project
- Students build and deploy a deep learning model in a chosen domain (e.g., image classification, NLP task, etc.)

Participant’s Eligibility

AI and data science researchers, machine learning engineers, and academicians looking to gain deep learning expertise.

Program Outcomes

Mastery of fundamental deep learning concepts and architectures.
Ability to build, train, and optimize deep learning models.
Practical skills for real-world applications like image recognition and NLP.
Proficiency in using deep learning frameworks like TensorFlow and PyTorch.

Fee Structure

Standard Fee: INR ~~7,998~~ USD ~~118~~

Discounted Fee: INR 3,999 USD 59

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Key Takeaways

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.