Neural Network built from scratch using NumPy for Rainfall Prediction. Educational deep-dive into backpropagation and gradient descent.

11th place solution for the U-Tokyo Deep Learning Course MLP Competition (Top 0.8%). High-performance MLP implemented from scratch in NumPy, featuring AdamW, EMA, SWA, and MC Dropout.

An educational Python project developed during the Neural Network And Deep Learning course, this repository features the implementation of a neural network from scratch, without external libraries, and includes fundamental neural network algorithms.

15th place solution for the U-Tokyo Deep Learning Course Softmax Regression Competition (Top 0.9%). A highly optimized NumPy-only implementation featuring custom feature engineering (HOG/LBP), class-specific calibration, and distribution bias correction.