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Master Neural Networks for Building Modern AI Systems.
Book 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 the Basics of TensorFlow and Keras
10. Building End-to-end Image Segmentation Pipeline
11. Latest Advancements in AI
Index
From the Publisher
Know more about the book
Master Neural Networks for Building Modern AI Systems.
This book provides a comprehensive exploration of Artificial Intelligence (AI), beginning with a foundational understanding of its history, significant developments, and evolution into various sub-fields. The initial chapters lay down the theoretical groundwork, delineating between AI and Deep Learning and elucidating the basic concepts and models like neuron-inspired networks. Progressing sequentially, it offers practical insights into setting up Python workflows for AI development, focusing on installing essential packages and configuring development environments. It introduces foundational Python libraries and programming concepts crucial for AI development and data science, enhancing comprehension through web scraping, regex, and multithreading discussions.
Moving forward, the book delves deeper into advanced topics, covering effective neural network training concepts, dimensionality reduction techniques, and unsupervised learning.
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. 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.
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.
About the Author
Vishal Rajput is a recognized 3 x top 50 AI writer on Medium and a fervent explorer and practitioner of Artificial Intelligence. He holds an advanced master’s in AI from the globally acclaimed KU Leuven, Belgium. Since venturing into AI in 2016, Vishal has balanced academic rigor with industrial applicability, contributing to eight research papers in international journals and book chapters and amassing over six years of experience. He has collaborated with distinguished research labs such as SONY R&D and MIRZ UZ Leuven, exploring and contributing to various aspects of AI. Outside his professional and academic life, Vishal is an active speaker at AI meetups and events, serves as a mentor in AI, and leads innovation and AI development at a drone-based startup. With a wealth of knowledge and a plethora of experiences, he extends a warm invitation to all, offering a collaborative exploration and enriched appreciation of the unprecedented field of Artificial Intelligence.
Meet the Technical Reviewers
Nehaa Bansal is a trailblazing thought leader and data scientist, driven by a relentless passion for early innovation. With a wealth of experience spanning multiple industries including banking, finance, telecom, and insurance, Nehaa has mastered the art of developing predictive models that drive impactful outcomes. Her ability to excel both as an independent contributor and a collaborative team player sets her apart in the field.
Nehaa’s academic journey showcases a string of remarkable achievements. Graduating at the top of her class, she obtained a bachelor’s degree in computer science, laying a strong foundation for her future endeavors. Building upon her academic success, she further honed her skills by earning a master’s in data science from the esteemed BITS Pilani. At the core of Nehaa’s professional ethos lie values that shape her every action. She thrives on taking ownership, putting people first, and asking the fundamental question of “why” before embarking on any endeavor.
Pradeepta Mishra
Pradeepta Mishra is the Co-Founder and Chief Architect of Datasafeguard.ai a California headquartered start-up, leading a group of data scientists, computational linguistics experts, and machine learning and deep learning experts in building artificial intelligence-driven products for data privacy and synthetic fraud prevention. He was awarded “India’s Top – 40Under40DataScientists” by Analytics India Magazine for two years in a row in 2019 and 2020. As an inventor, he has filed 14 patents in different global locations, out of which 4 are granted. He is the author of nine books; his first book has been recommended in the HSLS center at the University of Pittsburgh, PA, USA. His 4th book #PytorchRecipes was published by Apress and added to Buswell Library, IL, USA. His fifth book #Practical Explainable AI using Python was recently published by Apress and has been recognized as a textbook for Barcelona Technology School’s (BTS) big data analytics course.
Publisher : Orange Education Pvt Ltd (November 4, 2023)
Language : English
Paperback : 401 pages
ISBN-10 : 9391246540
ISBN-13 : 978-9391246549
Item Weight : 1.52 pounds
Dimensions : 7.5 x 0.91 x 9.25 inches
