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Master Neural Networks for Building Modern AI Systems.
?Book DescriptionThis 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 learning. 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 Contents1. Understanding AI History2. Setting up Python Workflow for AI Development3. Python Libraries for Data Scientists4. Foundational Concepts for Effective Neural Network Training5. Dimensionality Reduction, Unsupervised Learning and Optimizations6. Building Deep Neural Networks from Scratch7. Derivatives, Backpropagation, and Optimizers8. Understanding Convolution and CNN Architectures9. Understanding the Basics of TensorFlow and Keras10. Building End-to-end Image Segmentation Pipeline11. Latest Advancements in AI Index
?Book DescriptionThis 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 learning. 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 Contents1. Understanding AI History2. Setting up Python Workflow for AI Development3. Python Libraries for Data Scientists4. Foundational Concepts for Effective Neural Network Training5. Dimensionality Reduction, Unsupervised Learning and Optimizations6. Building Deep Neural Networks from Scratch7. Derivatives, Backpropagation, and Optimizers8. Understanding Convolution and CNN Architectures9. Understanding the Basics of TensorFlow and Keras10. Building End-to-end Image Segmentation Pipeline11. Latest Advancements in AI Index