Overview

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. Inside You'll Learn: 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.

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