Deep Learning . April 1, 2025
Building a Neural Network Step by Step
Neural networks are the backbone of deep learning. In this post, I’ll walk through building a multi-layer neural network from scratch, focusing on the math and intuition behind the process.
Why Build From Scratch?
While libraries like TensorFlow and PyTorch make things easier, coding a neural net manually helps understand what’s happening under the hood.
Steps I Followed
Initialize Parameters – Random weights and biases.
Forward Propagation – Input → hidden layers → output.
Activation Functions – Used Sigmoid, Tanh, and ReLU to see their differences.
Loss Function – Binary cross-entropy for classification.
Backward Propagation – Computed gradients using chain rule.
Update Parameters – Gradient descent optimization.
Insights from Testing
- Sigmoid works well for small networks but suffers from vanishing gradients.
- Tanh centers data around zero, improving learning speed.
- ReLU is the most effective for deep networks, though prone to “dead neurons.”
Results
The network achieved strong accuracy on a binary classification dataset. More importantly, I gained clarity on how each layer and function contributes to the final prediction.
Final Note
Neural networks may look like a “black box,” but once you build one line by line, you realize it’s just math and logic combined. This project gave me confidence to dive deeper into advanced architectures like CNNs and RNNs.
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Kia Hoffman
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