Artificial Intelligence Algorithms

This repository contains implementations of various artificial intelligence algorithms using Jupyter notebooks. These notebooks are designed to help users understand and explore core concepts in AI, particularly machine learning algorithms. Each notebook focuses on a specific algorithm or neural network concept, with code created using Google Colaboratory.

Table of Contents

• Gradient Descent
• K-Nearest Neighbors (KNN)
• Neuron Model
• Perceptron
• Weight Initialization
• Gradients
• Network
• Updated Network

Files

1. Gradient_Descent.ipynb

• Description: Demonstrates the implementation of the Gradient Descent algorithm. This optimization technique is fundamental to many machine learning algorithms, including neural networks.
• Contents:
  • Basic gradient descent concepts
  • Code for minimizing a loss function
  • Visualizations of the optimization process

2. KNN.ipynb

• Description: Implementation of the K-Nearest Neighbors (KNN) algorithm, a non-parametric method used for classification and regression.
• Contents:
  • How to use KNN for classification
  • Example use cases with small datasets
  • Evaluation of model accuracy

3. Neuron.ipynb

• Description: Introduction to the concept of a single artificial neuron, the building block of neural networks.
• Contents:
  • Neuron model implementation
  • Activation functions such as sigmoid and ReLU
  • Hands-on code demonstrating neuron computation

4. Perceptron.ipynb

• Description: Covers the basics of the perceptron, one of the earliest neural network models.
• Contents:
  • Perceptron learning rule
  • Binary classification using a perceptron
  • Training and testing the model on datasets

5. Weight_Initialization.ipynb

• Description: Discusses different strategies for initializing the weights of a neural network, an important factor in ensuring the model converges during training.
• Contents:
  • Various initialization techniques (e.g., Xavier, He initialization)
  • Impact of initialization on the training process
  • Code demonstrations

6. grad.ipynb

• Description: Explores the concept of gradients and their role in optimizing machine learning models.
• Contents:
  • Gradient computation
  • Applications of gradients in machine learning
  • Practical examples and visualizations

7. network.ipynb

• Description: Provides a basic implementation of a neural network with a focus on the forward and backward propagation processes.
• Contents:
  • Neural network architecture
  • Forward propagation, backpropagation, and gradient descent
  • Training and evaluation of the model

8. updated_network.ipynb

• Description: An updated and improved version of the neural network model from network.ipynb, with added features and optimizations.
• Contents:
  • Enhanced architecture
  • Additional layers and activation functions
  • Better training performance

Installation and Usage

1. Clone the repository:

   git clone https://github.com/yourusername/Artificial-Intelligence-Algorithms.git