The past decade has witnessed the broad adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight in their widespread implementation has resulted in some incidents and harmful outcomes that could have been avoided with proper risk management. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public. Learn technical approaches for responsible AI across explainability, model validation and debugging, bias management, data privacy, and ML security Learn how to create a successful and impactful AI risk management practice Get a basic guide to existing standards, laws, and assessments for adopting AI technologies, including the new NIST AI Risk Management Framework Engage with interactive resources on GitHub and Colab

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

While many introductory guides to AI are calculus books in disguise, this one mostly eschews the math. Instead, author Jeff Prosise helps engineers and software developers build an intuitive understanding of AI to solve business problems. Need to create a system to detect the sounds of illegal logging in the rainforest, analyze text for sentiment, or predict early failures in rotating machinery? This practical book teaches you the skills necessary to put AI and machine learning to work at your company. Applied Machine Learning and AI for Engineers provides examples and illustrations from the AI and ML course Prosise teaches at companies and research institutions worldwide. There's no fluff and no scary equations—just a fast start for engineers and software developers, complete with hands-on examples. This book helps you: Learn what machine learning and deep learning are and what they can accomplish Understand how popular learning algorithms work and when to apply them Build machine learning models in Python with Scikit-Learn, and neural networks with Keras and TensorFlow Train and score regression models and binary and multiclass classification models Build facial recognition models and object detection models Build language models that respond to natural-language queries and translate text to other languages Use Cognitive Services to infuse AI into the apps that you write

Dive into the world of Intelligent Document Processing (IDP) with the power of AWS AI/ML. This book guides you from understanding the challenges of document processing to building effective IDP pipelines using advanced AWS APIs and Python. Through hands-on projects and real-world applications, this book will equip you with the skills needed to automate and unlock value from your document workflows. What this Book will help me do Understand the stages and challenges of the Intelligent Document Processing pipeline. Learn how to automate document processing workflow using AWS AI services. Acquire practical insights into Python libraries for document processing. Discover industry applications including healthcare and financial sectors. Develop the skill to solve real-world IDP problems with AI/ML. Author(s) Sonali Sahu is a seasoned AI/ML consultant and author with a focus on innovative technologies for industry problems. With extensive hands-on project experience and deep expertise in AWS AI/ML tools, she bridges the gap between theory and application. Her writing is approachable and practical, aimed to empower technical practitioners to excel. Who is it for? This book is aimed at developers, data scientists, and technical professionals wanting to leverage AWS AI/ML for document processing. Aimed at intermediate-level professionals, the content helps those with a working knowledge of Python or AI tools to enhance their skills. Whether you're in healthcare, finance, or a similar field, this book equips you to address document-centric problems using cutting-edge solutions.

The aim of this book is to teach core concepts of machine learning while focuses on modern applications. It is meant for anyone who wants to master machine learning by providing technical and practical insights.

‘The advent of machine learning-based AI systems demands that our industry does not just share toys, but builds a new sandbox in which to play with them.’ - Phil Bernstein

Machine Learning with PyTorch and Scikit-Learn is a comprehensive resource for developers looking to dive deep into the world of machine learning. It introduces foundational concepts alongside practical implementations using Python and leading libraries such as PyTorch and Scikit-Learn. With well-explained techniques and real-world examples, you'll gain the knowledge needed to design, build, and optimize machine learning systems. What this Book will help me do Understand and apply core concepts in machine learning using Scikit-Learn. Develop and deploy deep learning models using PyTorch efficiently. Configure and optimize neural networks, transformers, and GANs for various applications. Handle and preprocess data effectively for building robust models. Follow best practices for model evaluation, tuning, and deployment. Author(s) Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili are experienced professionals in the field of machine learning with extensive teaching and writing backgrounds. They bring their expertise in Python and machine learning frameworks like PyTorch to provide both theoretical and practical insights helpful for learners. Their combined knowledge ensures a thorough and engaging learning experience suited for aspiring data scientists. Who is it for? This book is tailored for Python developers and data scientists eager to master machine learning and deep learning techniques. If you're familiar with Python programming and possess fundamental knowledge of calculus and linear algebra, you will find this book incredibly insightful. Whether you're entering the field or seeking to enhance your expertise, this resource caters to your professional growth in building advanced machine learning systems.

Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. About the Technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the Book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's Inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the Reader For readers who know basic Python. No machine learning knowledge necessary. About the Author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Quotes Did you think machine learning is complicated and hard to master? It’s not! Read this book! Serrano demystifies some of the best-held secrets of the machine learning society. - Sebastian Thrun, Founder, Udacity The first step to take on your machine learning journey. - Millad Dagdoni, Norwegian Labour and Welfare Administration A nicely written guided introduction, especially for those who want to code but feel shaky in their mathematics. - Erik D. Sapper, California Polytechnic State University The most approachable introduction to machine learning I’ve had the pleasure to read in recent years. Highly recommended. - Kay Engelhardt, devstats

The focus of this book is to introduce Artificial Intelligence (AI) and Machine Learning (ML) technologies into the context of Business Management. The book gives insights into the implementation and impact of AI and ML to business leaders, managers, technology developers, and implementers.

Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books – the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you’re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book’s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: •How text generators create novel stories and articles •How deep learning systems learn to play and win at human games •How image classification systems identify objects or people in a photo •How to think about probabilities in a way that’s useful to everyday life •How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It’s the future of AI, and this book allows you to fully envision it.

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Explore the intersection of machine learning and algorithmic trading with "Machine Learning for Algorithmic Trading" by Stefan Jansen. This comprehensive guide walks you through applying predictive modeling and data analysis to uncover financial signals and build systematic trading strategies. By the end, you'll be equipped to design and implement machine learning-driven trading systems. What this Book will help me do Develop data-driven trading strategies using supervised, unsupervised, and reinforcement learning methods. Master techniques for extracting predictive features from market and alternative datasets. Gain expertise in backtesting and validating ML-based trading strategies in Python. Apply text analysis techniques like NLP to news articles and transcripts for financial insights. Optimize portfolio risk and returns using advanced Python libraries. Author(s) Stefan Jansen is a quantitative researcher and data scientist with extensive experience in developing algorithmic trading solutions. He specializes in leveraging machine learning to extract financial insights and optimize investment strategies. His practical approach to applying ML in trading is reflected in this comprehensive guide, helping readers navigate complex trading challenges. Who is it for? This book is crafted for Python developers, data scientists, and finance professionals looking to integrate machine learning into algorithmic trading. Ideal for those with a basic understanding of Python and ML principles, it guides readers in crafting data-driven trading strategies. It's especially useful for analysts aiming to harness diverse data types for financial applications.

O’Reilly usage analysis shows continued growth in AI/ML and early signs that organizations are experimenting with advanced tools and methods.

Dive deep into how machine learning is transforming the financial industry with 'Machine Learning for Finance'. This comprehensive guide explores cutting-edge concepts in machine learning while providing practical insights and Python code examples to help readers apply these techniques to real-world financial scenarios. Whether tackling fraud detection, financial forecasting, or sentiment analysis, this book equips you with the understanding and tools needed to excel. What this Book will help me do Understand and implement machine learning techniques for structured data, natural language, images, and text. Learn Python-based tools and libraries such as scikit-learn, Keras, and TensorFlow for financial data analysis. Apply machine learning for tasks like predicting financial trends, detecting fraud, and customer sentiment analysis. Explore advanced topics such as neural networks, generative adversarial networks (GANs), and reinforcement learning. Gain hands-on experience with machine learning debugging, products launch preparation, and addressing bias in data. Author(s) James Le None and Jannes Klaas are experts in machine learning applications in financial technology. Jannes has extensive experience training financial professionals on implementing machine learning strategies in their work and pairs this with a deep academic understanding of the topic. Their dedication to empowering readers to confidently integrate AI and machine learning into financial applications shines through in this user-focused, richly detailed book. Who is it for? This book is tailored for financial professionals, data scientists, and enthusiasts aiming to harness machine learning's potential in finance. Readers should have a foundational understanding of mathematics, statistics, and Python programming. If you work in financial services and are curious about applications ranging from fraud detection to trend forecasting, this resource is for you. It's designed for those looking to advance their skills and make impactful contributions in financial technology.

"Table of Contents: 1 Introduction to Machine Learning 2 Preparing to Model 3 Modelling and Evaluation 4 Basics of Feature Engineering 5 Brief Overview of Probability 6 B ayesian Concept Learning 7 Super vised Learning: Classification 8 Super vised

Neural networks are at the very core of deep learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple’s Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., YouTube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind’s AlphaGo). This lesson introduces artificial neural networks, starting with a quick tour of the very first ANN architectures, then covering topics such as training neural nets, recurrent neural networks, and reinforcement learning. This lesson will clarify what neural networks are and why you may want to use them.

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions

Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included. Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner Provides in-depth coverage of unsupervised and semi-supervised learning Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex