Angielski
Master Neural Networks for Building Modern AI Systems.
DESCRIPTION
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.
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.
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.
TABLE OF CONTENTS
1. Understanding AI History
2. Setting up Python Workflow for AI Development
3. Python Libraries for Data Scientists
4. Foundational Concepts for Effective Neural Network Training
5. Dimensionality Reduction, Unsupervised Learning and Optimizations
6. Building Deep Neural Networks from Scratch
7. Derivatives, Backpropagation, and Optimizers
8. Understanding Convolution and CNN Architectures
9. Understanding Basics of TensorFlow and Keras
10. Building End-to-end Image Segmentation Pipeline
11. Latest Advancements in AI
Index
