Unlock the Black Box of Artificial Intelligence.
Are you ready to move beyond simply calling API functions? Neural Networks & Deep Learning with Python Programming is the definitive guide for developers who want to understand the "why" and "how" behind the most powerful AI architectures in the world.
This is not just another theoretical textbook. This is a code-first masterclass. Volume 11 takes you on a journey from the mathematical roots of the artificial neuron to the cutting-edge of Generative AI. You won't just learn how to use PyTorch and TensorFlow; you will learn how to build neural networks from scratch using nothing but NumPy, ensuring you master the calculus of backpropagation and the mechanics of optimization.
What’s Inside This Volume?
Through rigorous theoretical explanations, detailed "from-scratch" implementations, and advanced application scripts, you will master:
- Foundations of Intelligence: Build Perceptrons and Multi-Layer Networks manually to internalize weights, biases, and the Chain Rule.
- Computer Vision Mastery: Architect Convolutional Neural Networks (CNNs) to mimic the visual cortex, mastering padding, pooling, and transfer learning with ResNet and VGG.
- Sequence Modeling: Conquer time-series data with RNNs, LSTMs, and GRUs, solving the vanishing gradient problem.
- The Transformer Revolution: dissect the "Attention Is All You Need" architecture, building Encoders, Decoders, and Multi-Head Attention mechanisms from the ground up.
- Generative AI & Art: Train Generative Adversarial Networks (GANs) and dive deep into the math behind Diffusion Models to understand how engines like Stable Diffusion function.
- Production Deployment: Learn how to bridge the gap between research and production by exporting models to ONNX and serving them with TorchServe.
Perfect For: Python developers, data scientists, and ML engineers who are tired of "black box" tutorials and want to possess the deep architectural knowledge required to innovate, debug, and deploy state-of-the-art AI systems.
You can read this book as a standalone.
All the source code is on GitHub.
Master the math. Write the code. Build the future.
Check also the other books in this series
