Build Neural Networks from the Ground Up-No Black Boxes, Just Code

If you've ever wanted to truly understand how deep learning works-not just use high-level libraries-Deep Learning by Code is your roadmap.

This hands-on guide teaches you how to implement the core building blocks of neural networks from scratch using pure Python and NumPy. You'll code every layer, activation function, and optimization algorithm yourself-gaining a deep, intuitive understanding of how modern AI really works under the hood.

Whether you're a developer, data science student, or aspiring machine learning engineer, this book gives you the confidence and skills to create and experiment with your own deep learning models-line by line.

• How neural networks process information, learn patterns, and make predictions

• Step-by-step construction of forward and backward propagation

• Implementing activation functions, loss functions, and optimizers by hand

• Building feedforward, convolutional, and recurrent neural networks

• Understanding gradient descent, backpropagation, and weight updates

• Creating training loops without relying on frameworks

• Visualizing training behavior and model accuracy

• Transitioning from raw code to real-world applications

• Bonus: Compare your custom models to PyTorch and TensorFlow

This isn't just another tutorial-it's a deep dive into the mechanics of deep learning. You'll leave not just knowing how, but why.