Neural Networks for Everyone: Learn AI from Scratch

<p>Artificial intelligence is no longer optional in any professional field, but there is a vast gap between using third-party tools and truly understanding how they work. That gap keeps you dependent, unable to tailor models to specific problems or diagnose why a system fails.</p><p><strong>This book bridges that gap with conceptual reverse engineering:</strong> every component of a neural network is explained from its mathematical purpose to its practical implementation. You will see how gradient descent adjusts weights, why convolutions detect visual patterns, and how attention revolutionized sequential processing.</p><p><strong>Real code starting from chapter three:</strong> you will implement perceptrons, manual backpropagation, CNNs for image classification, and RNNs for time series. <strong>Modern frameworks without magic:</strong> TensorFlow and PyTorch are introduced after mastering the fundamentals, so you truly understand what they abstract. <strong>Current architectures dissected:</strong> ResNets, GANs, transformers, and attention mechanisms explained with clear diagrams and accessible math.</p><p>Designed for developers looking to pivot into AI, engineers needing to implement custom solutions, and technical professionals tired of superficial tutorials. Requirements: basic Python and a willingness to understand, not just copy.</p>