Deep Learning

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.

Requirements:

•  GPU (Graphics Processing Unit)

•  Memory

•  Storage

•  Deep Learning Framework

•  Python

•  Datasets

• Data Preprocessing

•  Understanding of Deep Learning Concepts

•  Programming and Python Skills

• Model Selection and Evaluation

• Training and Optimization

• Data Visualization

Deep learning algorithms are designed to automatically learn and improve from experience by analyzing large amounts of labeled or unlabeled data.

The learning process involves training a neural network on a dataset and adjusting the network's weights and biases to minimize the difference between predicted and actual outputs. This is typically achieved using optimization techniques like gradient descent and backpropagation.

One of the key advantages of deep learning is its ability to learn hierarchical representations of data. Deep neural networks can automatically learn multiple levels of abstraction, with each layer of the network learning increasingly complex features or concepts.

Curriculum:

• Module 1: Introduction

• Overview

• Introduction to Deep learning

• What is Data Structures

• Deep Learning Framework

•  Historical development and key milestones in deep learning

• Installation

• Set up your development environment

• Install Python packages and dependencies

• Install Python packages and dependencies

• PyTorch

• MXNet

• Install GPU support

• Additional libraries and tools

• Module 2: Architecture, Scenarios, and Admin Tools Deep learning

• Architecture & scenarios

•  Convolutional Neural Networks (CNNs)

• Convolutional Neural Networks (CNNs)

•  Transformers

• Autoencoders

•  Image Classification

• Object Detection

• Model Training and Tuning

•  . Model Interpretability and Explainability

• Module 3: Deep Learning Basics

• Variables

• Deep Learning Basics

• Activation functions

• Backpropagation algorithm

• Gradient descent optimization

• Module 4: Recurrent Neural Networks (RNNs) Deep learning

• Recurrent Neural Networks (RNNs)

• Long Short-Term Memory (LSTM) networks

• Applications in natural language processing and speech recognition

• Module 5: Training Deep Neural Networks Deep learning

• Regularization techniques (dropout, batch normalization)

• Optimization algorithms (Adam, RMSprop)

• Hyperparameter tuning

• Transfer learning and fine-tuning

• Module 6: Advanced Deep Learning Topics Deep learning

• Generative models (variational autoencoders, generative adversarial networks)

• Deep reinforcement learning

• Deep reinforcement learning

• Explainability and interpretability in deep learning

• Module 7: Hands-on Projects

• Implementing deep learning models using TensorFlow or PyTorch

• Image classification project

• Natural language processing project

• Optional: Custom project based on student's interest

• Module 8: : Evaluation and Assessment

• Quizzes and assignments

• Practical coding projects

• Final exam (theory and implementation)

• Module 9: :Prerequisites

• Basic understanding of linear algebra and calculus

• Familiarity with programming concepts (Python recommended)

• Some knowledge of machine learning concepts (e.g., supervised learning, optimization)

• Module 10: Placement Guide

• Tips to clear an Interview

• Common Interview questions and answers

• Deep Learning Interview Questions and Answers

• Resume Building Guide

• Career roadmap and certifications

• Attempt for Deep Learning Certification Exam

• Start applying for Jobs