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🤖 Machine Learning & Deep Learning from Scratch

A complete and organized repository with Machine Learning and Deep Learning algorithm implementations from scratch, practical projects, and educational notebooks.

📚 Repository Structure

This repository is organized into three main areas:

🔷 1. Machine Learning (Classical Algorithms)

Implementations of fundamental machine learning algorithms.

📊 Supervised Learning

  • Regression

    • Linear Regression (Gradient Descent & Normal Equation)
    • Locally Weighted Linear Regression
    • Softmax Regression

  • Classification

    • Logistic Regression (Gradient Descent & Newton's Method)
    • Naive Bayes

🔍 Unsupervised Learning

  • Clustering
    • K-Means (C++ implementation)

🎯 Nearest Neighbors

  • K-Nearest Neighbors (KNN) - C++ implementation

⚙️ C++ Implementations

Low-level C++ implementations for optimized performance.

🔷 2. Deep Learning (Neural Networks and Deep Learning)

🧠 Fundamentals

  • Basic neural network implementation
  • Layers: Input, Hidden, Output
  • Neurons and network architecture

👁️ Computer Vision

Classic CNN Architectures:

  • LeNet
  • VGG16
  • GoogLeNet (Inception)
  • ResNet
  • EfficientNet

Practical Projects:

  • Melanoma Classification
  • Skin Cancer Classification
  • FP16 Optimization

OpenCV:

  • Basic tutorials: filters, colors, contours, edge detection
  • Projects: Color detection, Face anonymization
  • Various experiments

Utilities:

  • Data loaders
  • Mean and standard deviation calculation
  • Binary Focal Loss
  • Helper functions

🎨 Generative Models

  • Autoencoders
  • Fully Connected GAN
  • DCGAN (Deep Convolutional GAN)

💬 NLP (Natural Language Processing)

Fundamentals:

  • N-grams
  • Data gathering and preprocessing

Sequence Models:

  • RNN (Recurrent Neural Networks)
  • RNN from Scratch
  • LSTM (Long Short-Term Memory)
  • Sequence-to-Sequence
  • Sequence-to-Sequence with Attention

Transformers:

  • Transformers from Scratch
  • GPT-1 from Scratch
  • Transformers for Translation
  • Project: GPT trained on Wikipedia articles

Applications:

  • Spam Classifier (Naive Bayes)
  • Name Generator
  • Datasets and metrics

🎓 Specialized Techniques

  • Transfer Learning
  • Multi-label Classification
  • Transpose Convolution

🔷 3. Recommendation Systems

  • Popularity-based recommendation system

📂 Datasets

All datasets are centralized in the data/ folder:

  • images/: MNIST and other images
  • tabular/: Iris.csv and other tabular datasets

🚀 How to Use

Prerequisites

Python:

pip install numpy pandas matplotlib scikit-learn
pip install torch torchvision
pip install tensorflow
pip install opencv-python
pip install nltk

C++ (for native implementations):

  • C++ Compiler (g++, MSVC, etc.)
  • CMake (optional)

Running Notebooks

  1. Install Jupyter:
pip install jupyter notebook
  1. Navigate to desired folder:
cd 2-Deep-Learning/computer_vision/architectures
jupyter notebook

Compiling C++ Code

cd 1-Machine-Learning/cpp_implementations
g++ -std=c++17 -o program main.cpp knn.cpp kmeans.cpp data_handler.cpp
./program

📖 Topics by Area

Classical Machine Learning

  • Linear and Logistic Regression
  • Distance-based algorithms (KNN)
  • Probabilistic models (Naive Bayes)
  • Clustering (K-Means)

Deep Learning - Computer Vision

  • Fundamental CNN architectures
  • Transfer Learning
  • GANs and generative models
  • Medical projects (skin cancer detection)
  • OpenCV and image processing

Deep Learning - NLP

  • Classical language models (N-grams)
  • RNNs and LSTMs
  • Attention Mechanisms
  • Transformers and GPT
  • Practical applications

🎯 Repository Goals

  1. Educational: Didactic implementations from scratch
  2. Practical: Real projects and applications
  3. Organized: Clear structure by topics and subtopics
  4. Complete: From fundamentals to advanced techniques

🛠️ Technologies Used

  • Languages: Python, C++
  • DL Frameworks: PyTorch, TensorFlow
  • Computer Vision: OpenCV
  • NLP: NLTK, Transformers
  • Data: NumPy, Pandas
  • Visualization: Matplotlib, Seaborn

📝 Detailed Directory Structure

Machine-Learning-Scratch/
├── data/                                      # Centralized datasets
│   ├── images/                               # MNIST, etc.
│   └── tabular/                              # CSV files
│
├── 1-Machine-Learning/                        # Classical ML
│   ├── supervised/
│   │   ├── regression/                       # Regression algorithms
│   │   ├── classification/                   # Classification algorithms
│   │   └── examples/                         # Practical examples
│   ├── unsupervised/
│   │   ├── clustering/                       # K-means
│   │   └── dimensionality_reduction/         # PCA, etc.
│   ├── nearest_neighbors/                    # KNN
│   └── cpp_implementations/                  # C++ implementations
│
├── 2-Deep-Learning/                           # Deep Learning
│   ├── fundamentals/                         # Basic neural networks
│   │   └── layers/                           # Layer implementations
│   ├── computer_vision/
│   │   ├── architectures/                    # Classic CNNs
│   │   ├── projects/                         # Practical projects
│   │   ├── opencv/                           # OpenCV
│   │   └── utils/                            # Utilities
│   ├── generative_models/                    # GANs, Autoencoders
│   ├── nlp/
│   │   ├── fundamentals/                     # N-grams, preprocessing
│   │   ├── sequence_models/                  # RNN, LSTM
│   │   ├── transformers/                     # Transformers, GPT
│   │   └── applications/                     # Practical projects
│   └── specialized/                          # Specialized techniques
│
├── 3-Recommendation-Systems/                  # Recommendation systems
│
└── README.md                                  # This file

🤝 Contributions

This is a personal study repository, but suggestions and improvements are welcome!

📄 License

This project is for educational purposes.

📧 Contact

Last updated: October 2025