Master Neural Networks for Building Modern AI Systems.
KEY FEATURES
? Comprehensive Coverage of Foundational AI Concepts and Theories.
? In-Depth Exploration of Maths Behind Neural Network Mathematics.
? Effective Strategies for Structuring Deep Learning Code.
? Real-World Applications of AI Principles and Techniques.
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.
WHAT WILL YOU LEARN
? Leverage TensorFlow and Keras while building the foundation for creating AI pipelines.
? Explore advanced AI concepts, including dimensionality reduction, unsupervised learning, and optimization techniques.
? Master the intricacies of neural network construction from the ground up.
? Dive deeper into neural network development, covering derivatives, backpropagation, and optimization strategies.
? Harness the power of high-level AI libraries to develop production-ready code, allowing you to accelerate the development of AI applications.
? Stay up-to-date with the latest breakthroughs and advancements in the dynamic field of artificial intelligence.
WHO IS THIS BOOK FOR?
This book serves as an ideal guide for software engineers eager to explore AI, offering a detailed exploration and practical application of AI concepts using Python. AI researchers will find this book enlightening, providing clear insights into the mathematical concepts underlying AI algorithms and aiding in writing production-level code. This book is designed to enhance your skills and knowledge to create sophisticated, AI-powered solutions and advance in the multifaceted field of AI.
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
