Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.In the book, readers will learn how to:Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual 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.

    One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. You’ll explore the basic operations and common functions of Spark’s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation

    While some marvel at these changes, envisioning consciousness downloaded into a computer or humans "beamed" Star Trek-style, others view them with horror, seeing monsters brooding in the machines. In How We Became Posthuman, N. Katherine Hayles separates hype from fact, investigating the fate of embodiment in an information age.Hayles relates three interwoven stories: how information lost its body, that is, how it came to be conceptualized as an entity separate from the material forms that carry it; the cultural and technological construction of the cyborg; and the dismantling of the liberal humanist "subject" in cybernetic discourse, along with the emergence of the "posthuman."Ranging widely across the history of technology, cultural studies, and literary criticism, Hayles shows what had to be erased, forgotten, and elided to conceive of information as a disembodied entity. Thus she moves from the post-World War II Macy Conferences on cybernetics to the 1952 novel Limbo by cybernetics aficionado Bernard Wolfe; from the concept of self-making to Philip K. Dick's literary explorations of hallucination and reality; and from artificial life to postmodern novels exploring the implications of seeing humans as cybernetic systems.Although becoming posthuman can be nightmarish, Hayles shows how it can also be liberating. From the birth of cybernetics to artificial life, How We Became Posthuman provides an indispensable account of how we arrived in our virtual age, and of where we might go from here.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    The application of the developed guidelines within the framework of ITIL® allows IT departments to improve their overall efficiency; from the way IT infrastructure is created and managed to how individual service interactions within the business are conducted. Through the use of Key Performance Indicators (KPIs), ITIL® measures the effectiveness of an IT organization by analyzing data related to performance, issues, process improvement and progress. Given its framework structure, one of the key strengths of ITIL® is its ability to be adapted and modified to fit the unique structure and needs of an organization. By not prescribing a specific set of rules, the dynamic nature of ITIL® affords the ability to be scalable and flexible within an organization. The goal of this book is simple: breakdown ITIL® into easy to follow concepts and examples that anyone can follow. Put simply This Book Will Become Your ITIL® Bible. ITIL® is quickly changing the way IT departments around the world are operated. The sooner your organization implements the framework, the sooner you will start to see improvements in both how business is conducted internally as well as with external stakeholders. For those within or outside of the IT field, This Book Will Break Down All of The Concepts and Guidelines Included Within the ITIL® Framework Into Easy to Understand and Follow Pieces. Regardless of your prior experience, you will be completely knowledgeable with the ITIL® framework so that you will be able to able to begin implementing the guidelines by the end of this book. If you are an IT professional looking to increase your skill set and increase your contributions to your organization – this book is for you. If you are preparing for the ITIL® Foundation Exam - this book is for you. If you are a business owner who wants to improve the efficiency and effectiveness of your IT department – this book is for you. Here Is A Preview Of What You'll Learn... The Principles & Philosophies That Define The ITIL® Framework The Tools & Techniques You Need To Understand The ITIL® Guidelines How ITIL® Can Directly Affect Your Employees and Customers A Thourough Explanition of Each Process Within ITIL® The Specific KPIs That Are Relevant To Each Process The Top Mistakes to AVOID That Those New To ITIL® Make A FREE Gift from ClydeBank Media Worth Over $250 Dollars! Much, Much More! Our Personal Guarantee We are so confident that methods outlined in this book will help you understand ITIL® that we're willing to let you try the book risk-free. If you are not fully satisfied with the product, simply let us know and we will provide a 100% full refund. That’s right, a 100% Money-Back Guarantee! What reason do you have to not give this book a try? Scroll Up To The Top Of The Page And Click The Orange "Buy Now" or "Read For Free" Icon On The Right Side Right Now! ClydeBank Media LLC All Rights Reserved

    In 1935, aged 22, he developed the mathematical theory upon which all subsequent stored-program digital computers are modeled.At the outbreak of hostilities with Germany in September 1939, he joined the Government Codebreaking team at Bletchley Park, Buckinghamshire and played a crucial role in deciphering Engima, the code used by the German armed forces to protect their radio communications. Turing's work on the versionof Enigma used by the German navy was vital to the battle for supremacy in the North Atlantic. He also contributed to the attack on the cyphers known as Fish, which were used by the German High Command for the encryption of signals during the latter part of the war. His contribution helped toshorten the war in Europe by an estimated two years.After the war, his theoretical work led to the development of Britain's first computers at the National Physical Laboratory and the Royal Society Computing Machine Laboratory at Manchester University.Turing was also a founding father of modern cognitive science, theorizing that the cortex at birth is an unorganized machine which through training becomes organized into a universal machine or something like it. He went on to develop the use of computers to model biological growth, launchingthe discipline now referred to as Artificial Life.The papers in this book are the key works for understanding Turing's phenomenal contribution across all these fields. The collection includes Turing's declassified wartime Treatise on the Enigma; letters from Turing to Churchill and to codebreakers; lectures, papers, and broadcasts which opened upthe concept of AI and its implications; and the paper which formed the genesis of the investigation of Artifical Life.

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the “black box” API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans. You will be able to understand the “brain” behind state-of-the-art Artificial Intelligence. Furthermore, unlike other courses that assume advanced knowledge of Calculus and leverage complex mathematical notation, if you’re a Python hacker who passed high-school algebra, you’re ready to go. And at the end, you’ll even build an A.I. that will learn to defeat you in a classic Atari game.

    They are vehicles, a series of hypothetical, self-operating machines that exhibit increasingly intricate if not always successful or civilized behavior. Each of the vehicles in the series incorporates the essential features of all the earlier models and along the way they come to embody aggression, love, logic, manifestations of foresight, concept formation, creative thinking, personality, and free will. In a section of extensive biological notes, Braitenberg locates many elements of his fantasy in current brain research.

    This second edition is appropriate as a supplementary (companion) text for any college-level course covering digital signal processing.

    She can't change her own traumatic past, but she can save other women. When Tomo introduces a deceptive new product that preys on users' fears to drive up its own revenue, Angie sees Tomo for what it really is--another evil abuser. Using her coding and hacking expertise, she decides to destroy Tomo by building a new social network that is completely distributed, compartmentalized, and unstoppable. If she succeeds, it will be the end of all centralized power in the Internet. But how can an anti-social, one-armed programmer with too many dark secrets succeed when the world's largest tech company is out to crush her and a no-name government black ops agency sets a psychopath to look into her growing digital footprint?"

    Divided into two parts, the first part of the book introduces the programming language Prolog, while the second part teaches Artificial Intelligence using Prolog as a tool for the implementation of AI techniques. Prolog has its roots in logic, however the main aim of this book is to teach Prolog as a practical programming tool. This text therefore concentrates on the art of using the basic mechanisms of Prolog to solve interesting problems. The third edition has been fully revised and extended to provide an even greater range of applications, which further enhance its value as a self-contained guide to Prolog, AI or AI Programming for students and professional programmers alike.