Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops. This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently. Understand the distinct features of foundation model-enabled AI agents Discover the core components and design principles of AI agents Explore design trade-offs and implement effective multiagent systems Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

Discover how to build autonomous AI agents tailored for real-world tasks with 'AI Agents in Practice.' This book guides you through creating and deploying AI systems that go beyond chatbots to solve complex problems, using leading frameworks and practical design patterns. What this Book will help me do Understand and implement core components of AI agents, such as memory, tool integration, and context management. Develop production-ready AI agents for diverse applications using frameworks like LangChain. Design and implement multi-agent systems to enable advanced collaboration and problem-solving. Apply ethical and responsible AI techniques, including monitoring and human oversight, in agent development. Optimize performance and scalability of AI agents for production use cases. Author(s) Valentina Alto is an accomplished AI engineer with years of experience in AI systems design and implementation. Valentina specializes in developing practical solutions utilizing large language models and contemporary frameworks for real-world applications. Through her writing, she conveys complex ideas in an accessible manner, and her goal is to empower AI developers and enthusiasts with the skills to create meaningful solutions. Who is it for? This book is perfect for AI engineers, data scientists, and software developers ready to go beyond foundational knowledge of large language models to implement advanced AI agents. It caters to professionals looking to build scalable solutions and those interested in ethical considerations of AI usage. Readers with a background in machine learning and Python will benefit most from the technical insights provided.

Explore AI and Machine Learning fundamentals, tools, and applications in this beginner-friendly guide. Learn to build models in Python and understand AI ethics. Key Features Covers AI fundamentals, Machine Learning, and Python model-building Provides a clear, step-by-step guide to learning AI techniques Explains ethical considerations and the future role of AI in society Book Description This book is an ideal starting point for anyone interested in Artificial Intelligence and Machine Learning. It begins with the foundational principles of AI, offering a deep dive into its history, building blocks, and the stages of development. Readers will explore key AI concepts and gradually transition to practical applications, starting with machine learning algorithms such as linear regression and k-nearest neighbors. Through step-by-step Python tutorials, the book helps readers build and implement models with hands-on experience. As the book progresses, readers will dive into advanced AI topics like deep learning, natural language processing (NLP), and generative AI. Topics such as recommender systems and computer vision demonstrate the real-world applications of AI technologies. Ethical considerations and privacy concerns are also addressed, providing insight into the societal impact of these technologies. By the end of the book, readers will have a solid understanding of both the theory and practice of AI and Machine Learning. The final chapters provide resources for continued learning, ensuring that readers can continue to grow their AI expertise beyond the book. What you will learn Understand key AI and ML concepts and how they work together Build and apply machine learning models from scratch Use Python to implement AI techniques and improve model performance Explore essential AI tools and frameworks used in the industry Learn the importance of data and data preparation in AI development Grasp the ethical considerations and the future of AI in work Who this book is for This book is ideal for beginners with no prior knowledge of AI or Machine Learning. It is tailored to those who wish to dive into these topics but are not yet familiar with the terminology or techniques. There are no prerequisites, though basic programming knowledge can be helpful. The book caters to a wide audience, from students and hobbyists to professionals seeking to transition into AI roles. Readers should be enthusiastic about learning and exploring AI applications for the future.

The book is essential for anyone seeking to understand and leverage the transformative power of intelligent automation technologies, providing crucial insights into current trends, challenges, and effective solutions that can significantly enhance operational efficiency and decision-making within organizations. Intelligent automation systems, also called cognitive automation, use automation technologies such as artificial intelligence, business process management, and robotic process automation, to streamline and scale decision-making across organizations. Intelligent automation simplifies processes, frees up resources, improves operational efficiencies, and has a variety of applications. Intelligent automation systems aim to reduce costs by augmenting the workforce and improving productivity and accuracy through consistent processes and approaches, which enhance quality, improve customer experience, and address compliance and regulations with confidence. Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence explores the significant role, current trends, challenges, and potential solutions to existing challenges in the field of intelligent automation systems, making it an invaluable guide for researchers, industry professionals, and students looking to apply these innovative technologies. Readers will find the volume: Offers comprehensive coverage on intelligent automation systems using computer vision and AI, covering everything from foundational concepts to real-world applications and ethical considerations; Provides actionable knowledge with case studies and best practices for intelligent automation systems, computer vision, and AI; Explores the integration of various techniques, including facial recognition, natural language processing, neuroscience and neuromarketing. Audience The book is designed for AI and data scientists, software developers and engineers in industry and academia, as well as business leaders and entrepreneurs who are interested in the applications of intelligent automation systems.

In just a few short years, AI has transformed software development, and snazzy new tools continue to arrive, with no let-up in sight. How, as a software engineer, product builder, or CTO, do you keep up? This practical book is the result of Sergio Pereira's mission to test every AI tool he could find and provide practitioners with much-needed guidance through the commotion. Generative AI for Software Development focuses on AI tool comparisons, practical workflows, and real-world case studies, with each chapter encompassing critical evaluations of the tools, their use cases, and their limitations. While product reviews are always relevant, the book goes further and delivers to readers a coherent framework for evaluating the tools and workflows of the future, which will continue to complicate the work of software development. Learn how code generation and autocompletion assistants are reshaping the developer experience Discover a consistent method for rating code-generation tools based on real-world coding challenges Explore the GenAI tools transforming UI/UX design and frontend development Learn how AI is streamlining code reviews and bug detection Review tools that are simplifying software testing and QA Explore AI for documentation and technical writing Understand how modern LLMs have redefined what chatbots can do

This book provides a comprehensive and practical guide to creating cutting-edge AI agents combining advanced technologies such as LLMs, retrieval-augmented generation (RAG), and knowledge graphs. By reading this book, you'll gain a deep understanding of how to design and build AI agents capable of real-world problem solving, reasoning, and action execution. What this Book will help me do Understand the foundations of LLMs, RAG, and knowledge graphs, and how they can be combined to build effective AI agents. Learn techniques to enhance factual accuracy and grounding through RAG pipelines and knowledge graphs. Develop AI agents that integrate planning, reasoning, and live tool usage to solve complex problems. Master the use of Python and popular AI libraries to build scalable AI agent applications. Acquire strategies for deploying and monitoring AI agents in production for reliable operation. Author(s) This book is written by Salvatore Raieli and Gabriele Iuculano, accomplished experts in artificial intelligence and machine learning. Both authors bring extensive professional experience from their work in AI-related fields, particularly in applying innovative AI methods to solve challenging problems. Through their clear and approachable writing style, they aim to make advanced AI concepts accessible to readers at various levels. Who is it for? This book is ideally suited for data scientists, AI practitioners, and technology enthusiasts seeking to deepen their knowledge in building intelligent AI agents. It is perfect for those who already have a foundational understanding of Python and general artificial intelligence concepts. Experienced professionals looking to explore state-of-the-art AI solutions, as well as beginners eager to advance their technical skills, will find this book invaluable.

This book is essential for anyone eager to understand the groundbreaking advancements in generative AI and its transformative effects across industries, making it a valuable resource for both professional growth and creative inspiration. Generative AI: Disruptive Technologies for Innovative Applications delves into the exciting and rapidly evolving world of generative artificial intelligence and its profound impact on various industries and domains. This comprehensive volume brings together leading experts and researchers to explore the cutting-edge advancements, applications, and implications of generative AI technologies. This volume provides an in-depth exploration of generative AI, which encompasses a range of techniques such as generative adversarial networks, recurrent neural networks, and transformer models like GPT-3. It examines how these technologies enable machines to generate content, including text, images, and audio, that closely mimics human creativity and intelligence. Readers will gain valuable insights into the fundamentals of generative AI, innovative applications, ethical and social considerations, interdisciplinary insights, and future directions of this invaluable emerging technology. Generative AI: Disruptive Technologies for Innovative Applications is an indispensable resource for researchers, practitioners, and anyone interested in the transformative potential of generative AI in revolutionizing industries, unleashing creativity, and pushing the boundaries of what’s possible in artificial intelligence. Audience AI researchers, industry professionals, data scientists, machine learning experts, students, policymakers, and entrepreneurs interested in the innovative field of generative AI.

Eager to learn AI and machine learning but unsure where to start? Laurence Moroney's hands-on, code-first guide demystifies complex AI concepts without relying on advanced mathematics. Designed for programmers, it focuses on practical applications using PyTorch, helping you build real-world models without feeling overwhelmed. From computer vision and natural language processing (NLP) to generative AI with Hugging Face Transformers, this book equips you with the skills most in demand for AI development today. You'll also learn how to deploy your models across the web and cloud confidently. Gain the confidence to apply AI without needing advanced math or theory expertise Discover how to build AI models for computer vision, NLP, and sequence modeling with PyTorch Learn generative AI techniques with Hugging Face Diffusers and Transformers

In "Mathematics of Machine Learning," you will explore the foundational mathematics essential for understanding and advancing in machine learning. The book covers linear algebra, calculus, and probability theory, offering readers clear explanations and practical Python-based implementations. What this Book will help me do Master fundamental linear algebra concepts such as matrices, eigenvalues, and vector spaces. Understand and apply principles of calculus, including multivariable functions and optimization. Gain confidence in utilizing probability theory concepts like Bayes' theorem and random distributions. Learn to implement mathematical concepts in Python to solve machine learning problems. Bridge the gap between theoretical mathematics and the practical demands of modern machine learning. Author(s) Tivadar Danka is a PhD mathematician with a specialized focus on machine learning applications. Known for his clear and engaging teaching style, Tivadar has a deep understanding of both mathematical rigor and practical ML challenges. His ability to break down complex ideas into comprehensible concepts has helped him reach thousands of learners globally. Who is it for? The book is perfect for data scientists, aspiring machine learning engineers, software developers working with ML, and researchers interested in advanced ML methodologies. If you have a basic understanding of algebra and Python programming, alongside some familiarity with machine learning concepts, this book will help you deepen your mathematical insight and elevate your practical applications.

In "Building Agentic AI Systems", you will explore how to design and create intelligent and autonomous AI agents that can reason, plan, and adapt. This book dives deep into the principles and practices necessary to unlock the potential of generative AI and agentic systems. From foundation to implementation, you'll gain valuable insights into cutting-edge AI architectures and functionalities. What this Book will help me do Understand the foundational concepts of generative AI and the principles of agentic systems. Develop skills to design AI agents capable of self-reflection, tool utilization, and adaptable planning. Explore strategies for ensuring ethical transparency and safety in autonomous AI systems. Learn practical techniques to build effective multi-agent AI collaborations with real-world applications. Gain insights into designing AI systems with scalability, adaptability, and minimal human intervention. Author(s) Anjanava Biswas and Wrick Talukdar are experts in AI development with years of experience working on generative AI frameworks and autonomous systems. They specialize in creating innovative AI solutions and contributing to AI best practices in the industry. Their dedication to teaching and clarity in writing make technical concepts accessible to developers at all levels. Who is it for? This book is ideal for AI developers, machine learning engineers, and software architects seeking to advance their understanding of designing and implementing intelligent autonomous AI systems. Readers should have a foundational understanding of machine learning principles and basic programming experience, particularly in Python, to follow the book effectively. Understanding of generative AI or large language models is helpful but not required. If you're aiming to build or refine your skills in agent-based AI systems and how they adapt, this book is for you.

Drawing from her experience at Google and Meta, Dr. Marily Nika delivers the definitive guide for product managers building AI and GenAI powered products. Packed with smart strategies, actionable tools, and real-world examples, this book breaks down the complex world of AI agents and generative AI products into a playbook for driving innovation to help product leaders bridge the gap between niche AI and GenAI technologies and user pain points. Whether you're already leading product teams or are an aspiring product manager, and regardless of your prior knowledge with AI, this guide will empower you to confidently navigate every stage of the AI product lifecycle. Confidently manage AI product development with tools, frameworks, strategic insights, and real-world examples from Google, Meta, OpenAI, and more Lead product orgs to solve real problems via agentic AI and GenAI capabilities Gain AI Awareness and technical fluency to work with AI models, LLMs, and the algorithms that power them; get cross-functional alignment; make strategic trade-offs; and set OKRs

If you're looking to build production-ready AI applications that can reason and retrieve external data for context-awareness, you'll need to master--;a popular development framework and platform for building, running, and managing agentic applications. LangChain is used by several leading companies, including Zapier, Replit, Databricks, and many more. This guide is an indispensable resource for developers who understand Python or JavaScript but are beginners eager to harness the power of AI. Authors Mayo Oshin and Nuno Campos demystify the use of LangChain through practical insights and in-depth tutorials. Starting with basic concepts, this book shows you step-by-step how to build a production-ready AI agent that uses your data. Harness the power of retrieval-augmented generation (RAG) to enhance the accuracy of LLMs using external up-to-date data Develop and deploy AI applications that interact intelligently and contextually with users Make use of the powerful agent architecture with LangGraph Integrate and manage third-party APIs and tools to extend the functionality of your AI applications Monitor, test, and evaluate your AI applications to improve performance Understand the foundations of LLM app development and how they can be used with LangChain

Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs. From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks. Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: Understand and implement AI agent behavior patterns Design and deploy production-ready intelligent agents Leverage the OpenAI Assistants API and complementary tools Implement robust knowledge management and memory systems Create self-improving agents with feedback loops Orchestrate collaborative multi-agent systems Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving. About the Technology Most production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems. About the Book In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI. What's Inside Knowledge management and memory systems Feedback loops for continuous agent learning Collaborative multi-agent systems Speech and computer vision About the Reader For intermediate Python programmers. About the Author Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning. Quotes This is about to become the hottest area of applied AI. Get a head start with this book! - Richard Davies, author of Prompt Engineering in Practice Couldn’t put this book down! It’s so comprehensive and clear that I felt like I was learning from a master teacher. - Radhika Kanubaddhi, Amazon An enlightening journey! This book transformed my questions into answers. - Jose San Leandro, ACM-SL Expertly guides through creating agent profiles, using tools, memory, planning, and multi-agent systems. Couldn’t be more timely! - Grigory Sapunov author of JAX in Action

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: Pick the right machine learning approach for your data Apply deep learning to tabular data Deploy tabular machine learning locally and in the cloud Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. About the Technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the Book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What's Inside Master XGBoost Apply deep learning to tabular data Deploy models locally and in the cloud Build pipelines to train and maintain models About the Reader For readers experienced with Python and the basics of machine learning. About the Authors Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Quotes

Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. About the Technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the Book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's Inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the Reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the Author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Quotes I love this book! It shows you how to implement common ML algorithms in plain Python with only the essential libraries, so you can see how the computation and math works in practice. - Junpeng Lao, Senior Data Scientist at Google I highly recommend this book. In the era of ChatGPT real knowledge of algorithms is invaluable. - Vatsal Desai, InfoDesk Explains algorithms so well that even a novice can digest it. - Harsh Raval, Zymr

"Deep Learning and AI Superhero" is an extensive resource for mastering the core concepts and advanced techniques in AI and deep learning using TensorFlow, Keras, and PyTorch. This comprehensive guide walks you through topics from foundational neural network concepts to implementing real-world machine learning solutions. You will gain hands-on experience and theoretical knowledge to elevate your AI development skills. What this Book will help me do Develop a solid foundation in neural networks, their structure, and their training methodologies. Understand and implement deep learning models using TensorFlow and Keras effectively. Gain experience using PyTorch for creating, training, and optimizing advanced machine learning models. Learn advanced applications such as CNNs for computer vision, RNNs for sequential data, and Transformers for natural language processing. Deploy AI models on cloud and edge platforms through practical examples and optimized workflows. Author(s) Cuantum Technologies LLC has established itself as a pioneer in creating educational resources for advanced AI technologies. Their team consists of experts and practitioners in the field, combining years of industry and academic experience. Their books are crafted to ensure readers can practically apply cutting-edge AI techniques with clarity and confidence. Who is it for? This book is ideally suited for software developers, AI enthusiasts, and data scientists who have a basic understanding of programming and machine learning concepts. It's perfect for those seeking to enhance their skills and tackle real-world AI challenges. Whether your goals are professional development, research, or personal learning, you'll find practical and detailed guidance throughout this book.

This book has a multidimensional perspective on AI solutions for business innovation and real-life case studies to achieve competitive advantage and drive growth in the evolving digital landscape. Artificial Intelligence-Enabled Businesses demonstrates how AI is a catalyst for change in business functional areas. Though still in the experimental phase, AI is instrumental in redefining the workforce, predicting consumer behavior, solving real-life marketing dynamics and modifications, recommending products and content, foreseeing demand, analyzing costs, strategizing, managing big data, enabling collaboration of cross-entities, and sparking new ethical, social and regulatory implications for business. Thus, AI can effectively guide the future of financial services, trading, mobile banking, last-mile delivery, logistics, and supply chain with a solution-oriented focus on discrete business problems. Furthermore, it is expected to educate leaders to act in an ever more accurate, complex, and sophisticated business environment with the combination of human and machine intelligence. The book offers effective, efficient, and strategically competent suggestions for handling new challenges and responsibilities and is aimed at leaders who wish to be more innovative. It covers the early stages of AI adoption by organizations across their functional areas and provides insightful guidance for practitioners in the suitable and timely adoption of AI. This book will greatly help to scale up AI by leveraging interdisciplinary collaboration with cross-functional, skill-diverse teams and result in a competitive advantage. Audience This book is for marketing professionals, organizational leaders, and researchers to leverage AI and new technologies across various business functions. It also fits the needs of academics, students, and trainers, providing insights, case studies, and practical strategies for driving growth in the rapidly evolving digital landscape.

Dive into the intelligence that powers artificial intelligence Artificial intelligence is swiftly moving from a sci-fi future to a modern reality. This edition of Artificial Intelligence For Dummies keeps pace with the lighting-fast expansion of AI tools that are overhauling every corner of reality. This book demystifies how artificial intelligence systems operate, giving you a look at the inner workings of AI and explaining the important role of data in creating intelligence. You'll get a primer on using AI in everyday life, and you'll also get a glimpse into possible AI-driven futures. What's next for humanity in the age of AI? How will your job and your life change as AI continue to evolve? How can you take advantage of AI today to make your live easier? This jargon-free Dummies guide answers all your most pressing questions about the world of artificial intelligence. Learn the basics of AI hardware and software, and how intelligence is created from code Get up to date with the latest AI trends and disruptions across industries Wrap your mind around what the AI revolution means for humanity, and for you Discover tips on using generative AI ethically and effectively Artificial Intelligence For Dummies is the ideal starting point for anyone seeking a deeper technological understanding of how artificial intelligence works and what promise it holds for the future.

In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation.

Explore how artificial intelligence can transform your cybersecurity strategies with "Artificial Intelligence for Cybersecurity". This book provides practical insights into applying AI methods to a variety of cybersecurity problems, from malware analysis to threat detection. By understanding these concepts, you'll gain the knowledge needed to protect your organization's data and networks effectively. What this Book will help me do Understand how AI methods can address cybersecurity concerns effectively. Develop practical skills using AI tools to combat cyber threats. Design AI-powered solutions for malware identification and anomaly detection. Navigate real-world applications of AI in cybersecurity scenarios. Recognize and mitigate common pitfalls while implementing AI methods in cybersecurity. Author(s) The authors, Bojan Kolosnjaji, Huang Xiao, Peng Xu, and Apostolis Zarras, are experts in machine learning and cybersecurity. With extensive backgrounds in both academia and industry, they bring a wealth of knowledge to the book. Their practical and educational approach makes complex AI and cybersecurity concepts accessible, empowering readers to apply these methods to real-world problems. Who is it for? This book is ideal for professionals in cybersecurity who are keen to integrate AI techniques into their frameworks and workflows. It's also suitable for machine learning enthusiasts who want to delve into the realm of cybersecurity. If you possess a basic understanding of Python programming and machine learning fundamentals, this book will guide you through to advanced concepts. Whether you are a student or an industry veteran, this book offers valuable insights for enhancing your cybersecurity strategies with AI.

Are you grappling with increasing productivity and enhancing creativity within your business processes? As businesses evolve in this digital age, the demand for swift, efficient, and innovative solutions is more pressing than ever. Traditional methods often fall short in keeping pace with the rapid changes and challenges that professionals face daily. Enter this report by Thomas Nield. This curated guide outlines the transformative power of generative AI in various business functions and serves as a much-needed solution to overcoming modern business hurdles. Discover how AI can be your ally in not just meeting but exceeding your productivity and creativity goals. You'll learn how to: Quickly find and use relevant images for presentations, blogs, and articles Save valuable time and refine your communications with AI-assisted email rewriting Easily distill large volumes of information into essential summaries Leverage AI for efficient data-gathering from the web, perfectly suited for analysis Utilize AI-generated text and visuals to craft compelling basic marketing materials

Artificial Intelligence (AI) revolves around creating and utilizing intelligent machines through science and engineering. This book delves into the theory and practical applications of computer science methods that incorporate AI across many domains. It covers techniques such as Machine Learning (ML), Convolutional Neural Networks (CNN), Deep Learning (DL), and Large Language Models (LLM) to tackle complex issues and overcome various challenges.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

This book teaches solutions architects how to effectively design and implement AI/ML solutions utilizing Google Cloud services. Through detailed explanations, examples, and hands-on exercises, you will understand essential AI/ML concepts, tools, and best practices while building advanced applications. What this Book will help me do Build robust AI/ML solutions using Google Cloud tools such as TensorFlow, BigQuery, and Vertex AI. Prepare and process data efficiently for machine learning workloads. Establish and apply an MLOps framework for automating ML model lifecycle management. Implement cutting-edge generative AI solutions using best practices. Address common challenges in AI/ML projects with insights from expert solutions. Author(s) Kieran Kavanagh is a seasoned principal architect with nearly twenty years of experience in the tech industry. He has successfully led teams in designing, planning, and governing enterprise cloud strategies, and his wealth of experience is distilled into the practical approaches and insights in this book. Who is it for? This book is ideal for IT professionals aspiring to design AI/ML solutions, particularly in the role of solutions architects. It assumes a basic knowledge of Python and foundational AI/ML concepts but is suitable for both beginners and seasoned practitioners. If you're looking to deepen your understanding of state-of-the-art AI/ML applications on Google Cloud, this resource will guide you.

Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively. Inside Math and Architectures of Deep Learning you will find: Math, theory, and programming principles side by side Linear algebra, vector calculus and multivariate statistics for deep learning The structure of neural networks Implementing deep learning architectures with Python and PyTorch Troubleshooting underperforming models Working code samples in downloadable Jupyter notebooks The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. About the Technology Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective. About the Book Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research. What's Inside The core design principles of neural networks Implementing deep learning with Python and PyTorch Regularizing and optimizing underperforming models About the Reader Readers need to know Python and the basics of algebra and calculus. About the Author Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe. Quotes Machine learning uses a cocktail of linear algebra, vector calculus, statistical analysis, and topology to represent, visualize, and manipulate points in high dimensional spaces. This book builds that foundation in an intuitive way–along with the PyTorch code you need to be a successful deep learning practitioner. - Vineet Gupta, Google Research A thorough explanation of the mathematics behind deep learning! - Grigory Sapunov, Intento Deep learning in its full glory, with all its mathematical details. This is the book! - Atul Saurav, Genworth Financial