deep-learning.md

Deep Learning Journey: From Regression to Advanced DNNs

Welcome to the Deep Learning Journey repository! This guide will take you through a structured learning path starting from basic regression models and advancing towards complex deep neural networks. The repository includes a series of tutorials, code samples, and exercises to solidify your understanding of each concept.

Table of Contents

1. Introduction
2. Prerequisites
3. Setup Instructions
4. Learning Path
   • Regression
   • Basic Neural Networks
   • Deep Neural Networks
   • Convolutional Neural Networks (CNNs)
   • Recurrent Neural Networks (RNNs) and LSTMs
   • Generative Adversarial Networks (GANs)
   • Deep Reinforcement Learning (DRL)
   • Transfer Learning
5. Project and Exercises
6. Additional Resources

Introduction

This repository aims to provide a practical introduction to deep neural networks (DNN) and extend your understanding of cutting-edge technology and concepts in the deep learning (DL) field. The hands-on exercises will help you apply neural networks to computer vision (CV) tasks, natural language processing (NLP) tasks, and domains with structured data.

Prerequisites

To make the most of this repository, you should have a solid understanding of:

• Multivariate calculus
• Linear algebra
• Probability and statistics
• Python programming

Prior experience with neural networks or machine learning, along with familiarity with data munging, numerical linear algebra, machine learning (ML), and visualization libraries, is highly recommended.

Learning Path

Regression

• Linear Regression

  • Concepts: Simple linear regression, multiple linear regression, gradient descent

• Logistic Regression

  • Concepts: Binary classification, cost function, optimization

Basic Neural Networks

• Introduction to Neural Networks

  • Concepts: Perceptron, activation functions, forward and backward propagation

Deep Neural Networks

• Feedforward Neural Networks

  • Concepts: Stacking layers, hidden units, regularization techniques

Convolutional Neural Networks (CNNs)

• Introduction to CNNs

  • Concepts: Convolutional layers, pooling layers, feature extraction

Recurrent Neural Networks (RNNs) and LSTMs

• Introduction to RNNs and LSTMs

  • Concepts: Sequence modeling, vanishing gradient problem, long-short term memory cells

Generative Adversarial Networks (GANs)

• Introduction to GANs
  • Notebook
  • Concepts: Generative models, adversarial training, GAN architecture

Deep Reinforcement Learning (DRL)

• Introduction to DRL

  • Concepts: Markov decision processes, policy gradients, Q-learning

Transfer Learning

• Introduction to Transfer Learning

Project and Exercises

• Hands-on Projects
  • In my Learning repository.

Additional Resources