Projects

• Now 2024 - Ongoing

  Convolutional Neural Network (from scratch in C++)

  github.com/madmax755/cnn-from-scratch

  Custom implementation of a Convolutional Neural Network (CNN) for image classification without ML libraries.

  Features:

  • Convolutional layers with configurable kernels
  • Pooling layers
  • Dense layers for classification
  • Tensor3D class for efficient 3D data handling
  • AdamW optimiser
  • Model serialisation (save/load functionality)

  Results:

  • Trained model on MNIST dataset (+augmentation) to achieve a 99.4% accuracy - see demo
• Oct 2024 - Nov 2024

  Gated Recurrent Unit (GRU) Neural Network (from scratch in C++)

  github.com/madmax755/gru-from-scratch

  Custom implementation of Gated Recurrent Unit (GRU) architecture for time series sequence processing without ML libraries.

  Features:

  • GRU cell implementation with MLP output layers
  • Configurable input, hidden, and output dimensions
  • AdamW optimiser
  • Financial metrics for stock prediction (profit/loss, directional accuracy)

  Results:

  • See demo to reveal the models strength and weaknesses on a sentiment analysis task
• Sep 2024 - Oct 2024

  Feedforward Neural Network (from scratch in C++)

  github.com/madmax755/mlp-from-scratch

  Custom implementation of a feedforward MLP neural network without external ML libraries.

  Features:

  • Flexible network topology with configurable layer sizes
  • Multiple activation functions (ReLU, Sigmoid, Softmax)
  • Advanced optimisers (SGD, SGD+Momentum, NAG, Adam, AdamW)
  • Multi-threaded training for improved performance
  • Model serialisation (save/load functionality)
  • Evaluation metrics (accuracy, precision, recall, F1 score)

  Results:

  • Acheived 91% peak accuracy on a large-scale handwritten digit classification dataset (MNIST).
• Sep 2024 - Ongoing

  Stock Price Predictor

  github.com/madmax755/stock-price-prediction-model

  • Engineered a comprehensive quantitative stock prediction model, processing 5+ years of daily price data for multiple stocks using Python, pandas, and scikit-learn.

  • Incorporated technical indicators (e.g., MACD, RSI, Bollinger Bands) and macroeconomic factors to capture complex market dynamics.

  • Applied statistical methods including VIF analysis and regularization techniques (Ridge, Lasso) to mitigate multicollinearity and prevent overfitting, redusing MSE by 41% over baseline models.

• Aug 2024 - Ongoing

  Self-hosted Web Server

  maxkendall.com

  • Created portfolio, minesweeper, and personal todo-list websites, self-hosted on a Raspberry Pi Zero W using Nginx as a reverse proxy and the cloudflare API to handle dynamic DNS allocation.

  • Used HTML, JavaScript, and CSS to create responsive, mobile-friendly user interfaces.