A complete educational resource for mastering Artificial Intelligence and Machine Learning, from foundational concepts to advanced neural networks. This repository provides hands-on implementations, mathematical foundations, and progressive learning paths for students, researchers, and practitioners.

This repository contains comprehensive implementations of AI/ML algorithms, neural networks, and data science tools, all built from scratch using pure Python and NumPy for maximum educational value.

• ✅ 100+ Implementations across 20+ categories
• ✅ Complete Neural Networks (10 advanced architectures)
• ✅ Classical ML Algorithms (comprehensive coverage)
• ✅ Data Science Pipeline (preprocessing to visualization)
• ⚠️ GenAI/LLM Components (planned for future releases)

learn/
├── 📁 algorithms/              # Core AI/ML algorithms
│   ├── java/                   # Java implementations
│   └── python/                 # Python implementations
├── 📁 neural_networks/         # Complete neural network architectures
│   ├── java/                   # Enterprise-grade Java implementations
│   └── python/                 # Educational Python implementations
├── 📁 machine_learning/        # Classical ML algorithms
│   ├── java/                   # Java ML implementations
│   └── python/                 # Python ML implementations
├── 📁 data_science/            # Data manipulation and analysis
├── 📁 deep_learning/           # Deep learning frameworks
├── 📁 computer_vision/         # CV algorithms and utilities
├── 📁 nlp/                     # Natural Language Processing
├── 📁 genai/                   # Generative AI components
├── 📁 statistics/              # Statistical analysis tools
├── 📁 visualization/           # Data visualization utilities
├── 📁 utilities/               # Helper functions and tools
├── 📄 LEARNING_GUIDE.md        # Comprehensive learning path
├── 📄 REPOSITORY_ANALYSIS.md   # Technical analysis
└── 📄 test_python_implementations.py # Complete test suite

• 🔥 Deep Convolutional Inverse Graphics Network (DCIGN) - Disentangled representation learning
• 🔄 Deconvolutional Networks - Semantic segmentation and upsampling
• 🏗️ Deep Belief Networks (DBN) - Layer-wise pretraining with RBMs
• 🏢 ResNet (Deep Residual Networks) - Skip connections for very deep networks
• 🌊 Echo State Networks (ESN) - Reservoir computing for time series
• ⚡ Extreme Learning Machine (ELM) - Single-pass training algorithm
• 💧 Liquid State Machine (LSM) - Spiking neural network reservoir
• 🔗 Markov Chain Neural Networks - Probabilistic sequence modeling
• 🧮 Neural Turing Machine (NTM) - Differentiable external memory
• 🎲 Restricted Boltzmann Machine (RBM) - Energy-based generative model

• Perceptron - Linear classification foundation
• Feed Forward Networks - Multi-layer perceptrons with backpropagation
• Convolutional Neural Networks (CNN) - Image processing and computer vision
• Recurrent Neural Networks (RNN) - Sequential data processing
• Long Short-Term Memory (LSTM) - Long-range dependency modeling
• Gated Recurrent Unit (GRU) - Simplified gating mechanism
• Autoencoders - Dimensionality reduction and representation learning
  • Basic Autoencoder
  • Variational Autoencoder (VAE)
  • Denoising Autoencoder
  • Sparse Autoencoder

• Generative Adversarial Networks (GAN) - Adversarial training
• Hopfield Networks - Associative memory
• Kohonen Self-Organizing Maps (SOM) - Unsupervised clustering
• Radial Basis Function Networks - Function approximation

• Linear Regression - Multiple solvers, regularization
• Logistic Regression - Binary/multiclass classification
• Support Vector Machines (SVM) - SMO algorithm, multiple kernels
• Decision Trees - Classification/regression with pruning
• Random Forest - Ensemble of decision trees
• Naive Bayes - Probabilistic classification
• K-Nearest Neighbors (KNN) - Instance-based learning

• K-Means Clustering - Centroid-based clustering
• Hierarchical Clustering - Agglomerative clustering
• DBSCAN - Density-based clustering
• Principal Component Analysis (PCA) - Dimensionality reduction
• Independent Component Analysis (ICA) - Signal separation

• Q-Learning - Value-based RL
• Policy Gradient Methods - Direct policy optimization

• Gradient Descent - Batch, stochastic, mini-batch variants
• Adam Optimizer - Adaptive moment estimation
• RMSprop - Root mean square propagation
• Momentum - Accelerated gradient descent
• AdaGrad - Adaptive gradient algorithm

• ReLU Family - ReLU, Leaky ReLU, ELU, Swish
• Classical Functions - Sigmoid, Tanh, Softmax
• Modern Variants - GELU, Mish, Softplus

• Regression - MSE, MAE, Huber Loss
• Classification - Cross-entropy, Hinge Loss
• Advanced - Focal Loss, Dice Loss

• Data Cleaning - Missing values, outliers, normalization
• Feature Engineering - Scaling, selection, transformation
• Feature Selection - Statistical and model-based methods

• Descriptive Statistics - Central tendency, dispersion
• Inferential Statistics - Hypothesis testing, confidence intervals
• Time Series Analysis - Trend, seasonality, forecasting

• Matplotlib Integration - Comprehensive plotting utilities
• Bokeh Interactive - Web-based interactive visualizations
• ggplot Style - Grammar of graphics implementation

1. Foundation - Python fundamentals, NumPy, statistics
2. Classical ML - Linear models, decision trees, evaluation
3. Data Science - Preprocessing, visualization, analysis
4. Projects - Real dataset analysis, algorithm comparison

1. Neural Networks - Perceptron to deep networks
2. Optimization - Gradient descent, backpropagation
3. Specialized Architectures - CNN, RNN, LSTM
4. Projects - Image classification, sequence prediction

1. Generative Models - Autoencoders, GANs, VAEs
2. Advanced Architectures - ResNet, attention mechanisms
3. Specialized Networks - Hopfield, SOM, Boltzmann machines
4. Projects - Generative art, anomaly detection

1. Research Implementations - DCIGN, NTM, LSM
2. Custom Architectures - Novel network designs
3. Performance Optimization - Efficient implementations
4. Capstone Projects - Original research contributions

# Required
Python 3.8+
NumPy >= 1.19.0
Matplotlib >= 3.3.0

# Optional (for enhanced features)
SciPy >= 1.5.0
Pandas >= 1.1.0

# Clone the repository
git clone <repository-url>
cd learn

# Start with fundamentals
python python_fundamentals/python_utilities.py

# Run comprehensive tests
python test_python_implementations.py

# Follow the learning guide
# See LEARNING_GUIDE.md for detailed path

# Neural Network Example
from neural_networks.python.feed_forward.feed_forward import FeedForwardNetwork

# Create and train network
nn = FeedForwardNetwork(layers=[784, 128, 64, 10])
nn.fit(X_train, y_train, epochs=100)
predictions = nn.predict(X_test)

# Machine Learning Example
from machine_learning.python.svm.svm import SVM

# Train SVM classifier
svm = SVM(kernel='rbf', C=1.0)
svm.fit(X_train, y_train)
accuracy = svm.score(X_test, y_test)

• Unit Tests - Individual algorithm validation
• Integration Tests - End-to-end pipeline testing
• Performance Tests - Efficiency benchmarks
• Mathematical Tests - Correctness verification

# Run all tests
python test_python_implementations.py

# Run specific category tests
python neural_networks/python/test_neural_networks.py

Each implementation includes:

• Working Examples - Practical demonstrations
• Synthetic Data - Generated test datasets
• Visualization - Training progress and results
• Performance Metrics - Accuracy, loss, convergence

• Detailed Equations - Mathematical formulations
• Algorithm Explanations - Step-by-step breakdowns
• Theoretical Background - Conceptual understanding
• Paper References - Original research citations

• Clean Implementation - Readable, well-structured code
• Comprehensive Comments - Line-by-line explanations
• Modular Design - Reusable components
• Error Handling - Robust error management

• Progressive Complexity - From simple to advanced
• Hands-on Examples - Practical applications
• Visualization Tools - Understanding through graphics
• Performance Analysis - Optimization insights

• Transformer Architecture - Multi-head attention, positional encoding
• Language Models - GPT-style, BERT-style architectures
• Tokenization - BPE, WordPiece, SentencePiece
• Text Generation - Sampling strategies, beam search

• Fine-tuning Techniques - LoRA, Adapters, Prompt Tuning
• Retrieval-Augmented Generation - RAG implementations
• Multimodal Models - Vision-Language integration
• Safety & Alignment - RLHF, Constitutional AI

• Distributed Training - Multi-GPU, multi-node support
• Model Optimization - Quantization, pruning, distillation
• Deployment Tools - Model serving, API integration
• MLOps Integration - Experiment tracking, model versioning

• 📁 20+ Categories - Comprehensive coverage
• 🔢 100+ Implementations - Extensive algorithm library
• 📝 50,000+ Lines - Substantial codebase
• 🧪 200+ Tests - Thorough validation
• 📖 Detailed Documentation - Complete explanations
• 🎯 Progressive Learning - Structured educational path

We welcome contributions! Please see our contribution guidelines:

1. Fork the repository
2. Create a feature branch
3. Implement with tests and documentation
4. Submit a pull request

• New Algorithms - Additional ML/AI implementations
• GenAI Components - Transformer, LLM architectures
• Performance Optimization - Efficiency improvements
• Documentation - Enhanced explanations and examples
• Testing - Additional test coverage
• Visualization - Better plotting and analysis tools

This project is licensed under the MIT License - see the LICENSE file for details.

• Research Community - For foundational papers and algorithms
• Open Source Projects - For inspiration and best practices
• Educational Institutions - For mathematical foundations
• Contributors - For ongoing improvements and feedback

• Documentation - Comprehensive guides and examples
• Issues - Bug reports and feature requests
• Discussions - Community Q&A and knowledge sharing
• Learning Path - Structured educational progression

🚀 Start your AI/ML journey today! Follow the Learning Guide for a structured path from beginner to expert.

This repository is continuously updated with new implementations, improvements, and educational content. Star ⭐ the repository to stay updated with the latest additions!