Ultimate Neural Network Programming with Python

<p><strong style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>Master Neural Networks for Building Modern AI Systems.</strong></p><p><br></p><p><strong style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>DESCRIPTION</strong></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>This book is a practical guide to the world of Artificial Intelligence (AI) unraveling the math and principles behind applications like Google Maps and Amazon. The book starts with an introduction to Python and AI demystifies complex AI math teaches you to implement AI concepts and explores high-level AI libraries.</span></p><p><br></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>Throughout the chapters readers are engaged with the book through practice exercises and supplementary learnings. The book then gradually moves to Neural Networks with Python before diving into constructing ANN models and real-world AI applications. It accommodates various learning styles letting readers focus on hands-on implementation or mathematical understanding.</span></p><p><br></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>This book isn't just about using AI tools; it's a compass in the world of AI resources empowering readers to modify and create tools for complex AI systems. It ensures a journey of exploration experimentation and proficiency in AI equipping readers with the skills needed to excel in the AI industry.</span></p><p><br></p><p><strong style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>TABLE OF CONTENTS </strong></p><p class=ql-align-justify><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>1. Understanding AI History</span></p><p class=ql-align-justify><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>2. Setting up Python Workflow for AI Development</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>3. Python Libraries for Data Scientists</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>4. Foundational Concepts for Effective Neural Network Training</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>5. Dimensionality Reduction Unsupervised Learning and Optimizations</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>6. Building Deep Neural Networks from Scratch</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>7. Derivatives Backpropagation and Optimizers</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>8. Understanding Convolution and CNN Architectures</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>9. Understanding Basics of TensorFlow and Keras</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>10. Building End-to-end Image Segmentation Pipeline</span></p><p><span style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>11. Latest Advancements in AI</span></p><p><strong style=background-color: rgba(0 0 0 0); color: rgba(0 0 0 1)>Index</strong></p><p><br></p>