The Science Of Ai Designing The Next Generation Of Intelligent Systems
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The Science of AI: Designing the Next Generation of Intelligent Systems
Author: Radhakrishnan Arikrishna Perumal
language: en
Publisher: Radhakrishnan Arikrishna Perumal
Release Date: 2025-01-01
Welcome to the next frontier of artificial intelligence — a world where machines don’t just think, they imagine, generate, and collaborate. Designing the Next Generation of Intelligent Systems is a definitive guide to understanding how generative AI is reshaping industries, unlocking human potential, and paving the way for intelligent systems that learn, create, and evolve. Written by Radhakrishnan Arikrishna Perumal, a Principal Software Architect, AI researcher, and acclaimed technology author, this book explores the science and real-world applications of generative AI — from foundational neural networks to the complex behaviors of Large Language Models (LLMs) like GPT-4, Claude, and Google Gemini. Whether you're a developer, data scientist, researcher, product manager, educator, or policy-maker, this book equips you with the knowledge, frameworks, and vision to navigate the transformative wave of generative AI. It bridges theory with practice, covering the mathematical foundations, model architectures, training pipelines, deployment workflows, ethical challenges, and enterprise use cases. What You’ll Learn: • The evolution of deep learning and the rise of generative models • Core concepts behind VAEs, GANs, Transformers, Diffusion Models, and LLMs • Inside look at frontier AI systems like GPT-4, Claude, and Gemini • How to build and fine-tune custom LLMs with open-source tools • Practical strategies for training, deploying, and scaling generative AI models using cloud and edge computing • Pipelines, LLMOps, and tools like Hugging Face, LangChain, and Azure AI Studio • Techniques for data augmentation, model optimization, and prompt engineering • Critical insights into security, interpretability, bias, and AI alignment • Use cases in healthcare, law, education, finance, and enterprise automation • The future of multimodal AI, ethical design, and human-AI collaboration Why This Book? Unlike generic AI introductions or academic research texts, this book offers both breadth and depth — structured to support readers who want a full-spectrum understanding of generative AI, from foundational knowledge to frontier innovation. It combines hands-on techniques with big-picture thinking, enriched with real-world examples, case studies, and tool comparisons. With over 35 chapters and 400+ pages of comprehensive, actionable insight, this is your essential guide to the future of artificial intelligence — a future where creativity, cognition, and computation converge. Who Should Read This Book? AI engineers and machine learning developers Cloud architects and DevOps professionals Data scientists and analysts Tech entrepreneurs and business leaders Academics, researchers, and students in AI and data science Policy-makers and professionals shaping the ethical use of AI About the Author: Radhakrishnan Arikrishna Perumal is a Principal Architect with 22+ years in IT and AI innovation. He has authored multiple books on technology, delivered keynotes at global conferences, and contributes to the advancement of responsible and scalable AI systems. His work spans enterprise architecture, generative AI research, and technical leadership in the cloud-native ecosystem. Start your journey into the next generation of intelligent systems. This isn’t just a book — it’s a blueprint for building the future with AI.
Fundamentals of Deep Learning
Author: Nikhil Buduma
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-05-25
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Artificial Intelligence in Society
The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.