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
Mentor Based

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

Intended For

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.

Mentors

AI, Computer Sciences Mentor

Keshan Srivastava

Freelance Educator & Mentor

Biography

AI Mentor

Rajnish Tandon

Bodhi Nexus (Founder)

Biography

AI Mentor

Pratish Jain

Rajiv Gandhi Proudyogiki Vishwavidyalaya

Biography

More Mentors

Fee Structure

Fee: INR 13,999 USD 176

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