Neural Network Framework
Overview
A custom deep learning framework built from scratch in Python, implementing the fundamental concepts of neural networks including backpropagation, various optimization algorithms, and modular layer architectures. This project demonstrates a deep understanding of the mathematical foundations behind modern machine learning.
Key Features
- Modular Architecture: Build networks with customizable layers including Dense, Convolutional, and Recurrent layers
- Optimization Algorithms: Implementation of SGD, Adam, RMSprop, and AdaGrad optimizers
- Automatic Differentiation: Computational graph for efficient backpropagation
- Regularization: Dropout, L1/L2 regularization, and batch normalization
- Activation Functions: ReLU, Sigmoid, Tanh, Softmax, and more
- Loss Functions: Cross-entropy, MSE, and custom loss implementations
Technical Details
The framework is designed with performance and educational clarity in mind. Each component is implemented using NumPy for efficient matrix operations while maintaining readable code that clearly demonstrates the underlying mathematics.
Key implementation highlights include:
- Forward and backward pass computation using chain rule
- Weight initialization strategies (Xavier, He initialization)
- Mini-batch gradient descent with momentum
- Learning rate scheduling and adaptive learning
Example Usage
Building a simple neural network for classification:
model = NeuralNetwork()
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy')
Future Enhancements
- GPU acceleration using CuPy
- Convolutional and recurrent layer implementations
- Model serialization and checkpointing
- Visualization tools for training metrics