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.

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

 

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.

ABOUT 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. Her agile mindset propels her to act swiftly, embrace failure as a learning opportunity, iterate continuously, and always strive to play fair. Driven by her deep-rooted passion for solving user problems, Nehaa leverages her expertise in analytics capabilities, product strategy, and leadership to craft innovative solutions. She approaches every challenge with unwavering dedication, consistently pushing the boundaries of what is possible.

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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. He delivered a keynote session at the Global Data Science Conference 2018, CA, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. Many of his books are translated and published in Japanese, Spanish, and Chinese on popular demand from authors.

He has delivered 500+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions, and community-arranged forums. He is visiting faculty and academic advisory board for AI-ML in M. Tech in AI course and Cyber Security course at Reva University, Bangalore, India, and also various other universities. Has mentored and trained over 2000 plus data scientists and AI engineers in last 18 years.