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Deep Learning & Machine Learning Algorithms

Status Python PyTorch GitHub Pages

Welcome to my repository for Deep Learning and Machine Learning algorithms! 🚀

This repository serves as a personal playground and portfolio where I upload code to practice, experiment with, and master various algorithms and frameworks. It is currently under active development, and new models and implementations will be added regularly.

📂 Repository Contents

Here is an overview of what has been implemented so far:

🧠 Generative Adversarial Networks (GANs)

  • Conditional GANs (CGANs): Implementation of Conditional Generative Adversarial Networks used for generating data (e.g., MNIST images) conditioned on class labels.
    • Location: GANs/CGANs.ipynb

📈 Classification & Regression

Try it here: https://slab10000.github.io/deep-learning-algorithms/classification-and-regression

Applied machine learning projects covering both classification and regression tasks.

  • Classification - Petfinder Dataset:

    • A project focusing on classifying pet adoption profiles.
    • Includes a Multi-Layer Perceptron (MLP) model saved in ONNX format (mlp_net_model.onnx).
    • Location: classification-and-regression/classification/

  • Regression - Songs Dataset:

    • A regression analysis project, likely predicting song popularity or features.
    • Utilizes XGBoost (Extreme Gradient Boosting) and includes the serialized model (xgboost_songs_model.onnx).
    • Location: classification-and-regression/regression/

🎬 Neural Input Optimization (NIO) - Movie Optimization

  • Project Blockbuster: NIO Optimisation for Movies:
    • A project that uses Neural Input Optimization (NIO) to reverse-engineer the optimal movie blueprint for maximizing both commercial success (Gross Revenue) and critical acclaim (IMDB Score).
    • Dataset: IMDB 5000 Movie Dataset from Kaggle (~5000 movies, 28 features)
    • Model Architecture: Residual Neural Network (ResNet) implemented in PyTorch with residual connections and dropout regularization
    • Optimization Goal: Find optimal movie characteristics (Budget, Cast, Genre) that maximize Return on Investment (ROI = Gross/Budget) while maintaining:
      • IMDB Score between 9.0 and 10.0
      • Budget between $20M and $200M
    • Key Results: The NIO algorithm identified that a mid-range budget (~$110M) with high star power and specific genre combinations yields optimal ROI (9.09x) while maintaining critical acclaim
    • Location: NIO/

🖼️ Convolutional Neural Networks (CNNs) - Shape Classification

  • Geometric Shape Classifier:
    • A CNN-based image classification project that identifies geometric shapes by counting their number of sides (Triangles: 3, Squares: 4, Pentagons: 5, Hexagons: 6).
    • Dataset: 10,000 images of geometric shapes (128×128 pixels, resized to 64×64 for training) with corresponding labels in CSV format
    • Model Architecture: "ShapeClassifier" CNN with:
      • 3 convolutional layers (16 → 32 → 64 channels) with ReLU activation and MaxPooling
      • Fully connected layers (128 hidden units) with dropout (0.5) for regularization
      • 4-class output layer for shape classification
    • Training: 20 epochs with batch size 64, CrossEntropyLoss, Adam optimizer, GPU-accelerated
    • Performance: Achieved excellent results with:
      • Precision: 0.971
      • Recall: 0.971
      • F1 Score: 0.971
    • Custom Dataset: Implemented ShapesDataset class for loading images and mapping side counts to class indices
    • Location: CNN/

🔢 Tensor Operations

Foundational notebooks for understanding data manipulation and tensor math.

  • NumPy & PyTorch: Introductory notebooks covering the basics of NumPy arrays and PyTorch tensor operations, essential for any Deep Learning workflow.
    • Location: tensor-operations/

🛠️ Tech Stack & Tools with

  • Languages: Python
  • Deep Learning Frameworks: PyTorch
  • Machine Learning Libraries: XGBoost, Scikit-Learn (implied)
  • Data Manipulation: NumPy, Pandas
  • Model Exchange: ONNX (Open Neural Network Exchange)
  • Environment: Jupyter Notebooks

🚧 Status

This project is in a Work-In-Progress state. I am constantly learning and adding new implementations, including but not limited to:

  • Computer Vision models (ViTs, more advanced CNNs)
  • NLP architectures (Transformers, RNNs)
  • Reinforcement Learning algorithms
  • More advanced GAN architectures

Feel free to explore the code!