My Secret AI Project: Neural Network Implementation

This post is password protected because it contains proprietary research and implementation details.

Project Overview

I’ve been working on implementing a neural network from scratch in Python, without using any machine learning libraries like TensorFlow or PyTorch. This project aims to understand the fundamental mathematics behind neural networks.

Architecture

class NeuralNetwork:
    def __init__(self, layers):
        self.layers = layers
        self.weights = []
        self.biases = []
        self.initialize_parameters()
    
    def initialize_parameters(self):
        for i in range(len(self.layers) - 1):
            weight_matrix = np.random.randn(self.layers[i], self.layers[i + 1]) * 0.01
            bias_vector = np.zeros((1, self.layers[i + 1]))
            self.weights.append(weight_matrix)
            self.biases.append(bias_vector)
    
    def forward_propagation(self, X):
        self.activations = [X]
        A = X
        
        for i in range(len(self.weights)):
            Z = np.dot(A, self.weights[i]) + self.biases[i]
            A = self.sigmoid(Z)
            self.activations.append(A)
        
        return A

Key Insights

1. Backpropagation is essentially the chain rule applied to compute gradients
2. Initialization of weights is crucial for convergence
3. Activation functions introduce non-linearity
4. Learning rate significantly impacts training performance

Results

After training on the XOR dataset, the network achieved 99.2% accuracy with the following hyperparameters:

• Learning rate: 0.1
• Hidden layers: [2, 4, 1]
• Epochs: 10000

Future Work

Next steps include:

• Implementing different activation functions
• Adding regularization techniques
• Experimenting with different optimizers
• Scaling to larger datasets

This project has deepened my understanding of the mathematical foundations of machine learning.