2_Neural_Networks.md

Module 2: Neural Networks & Deep Learning Basics

Overview

This module covers the fundamental concepts and practical implementation of neural networks and deep learning, essential for understanding and developing Large Language Models.

Core Topics

1. Coding & Algorithm Implementation

Master practical programming skills and algorithmic problem-solving essential for LLM development and optimization.

Key Concepts

• Data Structures
• Algorithms
• Problem Solving
• Code Optimization
• Python Programming
• DSA Concepts

Learning Sources

Essential	Optional

2. Neural Network Fundamentals

Understand the building blocks of neural networks and deep learning.

Key Concepts

• Neurons
• Layers
• Activation Functions
• Backpropagation
• Gradient Descent
• Loss Functions

Learning Sources

Essential	Optional

3. Neural Network Architectures

Survey various neural network structures fundamental to deep learning.

Key Concepts

• Multilayer Perceptron (MLP)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Activation Functions
• Network Design
• Architecture Selection

Learning Sources

Essential	Optional

Tools & Frameworks

Core	Additional

4. Training Dynamics & Optimization

Understand elements that influence model training, including loss functions and learning rate schedules.

Key Concepts

• Loss Functions
• Optimization Algorithms
• Learning Rate Scheduling
• Regularization Techniques
• Model Evaluation
• Performance Metrics

Learning Sources

Essential	Optional

Tools & Frameworks

Core	Additional