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.

    Deciding whether to build another power generation plant in the next five years requires forecasts of future demand. Scheduling staff in a call centre next week requires forecasts of call volumes. Stocking an inventory requires forecasts of stock requirements. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    - Build an Angular 2 application from scratch using TypeScript and the Angular command line interface. - Write code using the paradigm of reactive programming with RxJS and Observables. - Know how to Unit Test Angular 2 using Jasmine, Karma and the Angular Test Bed The first chapter in the course is a quickstart where you dive straight into writing your first Angular 2 application. We use the web editor plunker so you can get stuck in writing code ASAP. In this quickstart you'll get a 50,000 foot view of the major features of Angular 2. Then chapter by chapter we go much deeper into each of these features. I'll cover the theory for that feature, using plunker as much as possible so you can try out the code yourself in a browser. Then you'll practice what you've learnt with either an online quiz or a set of flash cards. You are going to learn all about:- - Typescript & ES6 Javascript. - Components & Binding - Directives - Dependancy Injection & Services - Angular Modules & Bootstrapping your Angular application. - SPAs & Routing - Angular CLI - Forms - Reactive Programming with RXJs - HTTP - Unit Testing The ideal student is an existing web developer, with some JavaScript knowledge that wants to add Angular 2 to their skill set. Or perhaps you are an existing Angular 1 developer who wants to level up to Angular 2. You do need to be comfortable with at least the ES5 version of JavaScript. We'll be using a UI framework called twitter bootstrap throughout the course but you still must know HTML and some CSS.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    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.

    Written in a way that even the financial novice can easily absorb, this is a new edition of the bestselling guide to understanding and using business accounts and accounting principles.

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    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.

    Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clustersDeploy code faster using tools like Docker

    These two have been the most distinguishing features of all the previous 6 editions of this book. Today's C programmer has to not only master the complexities and intricacies of the language but also has to contend with its usage in environments like Windows and Linux. This book covers all these three aspects of C Programming very well. This book doesn't assume any programming background. It begins with the basics and steadily builds the pace so that the reader finds it easy to handle complicated topics towards the end. Each chapter has been designed to create a deep and lasting impression on the reader's mind. "If taught through examples, any concept becomes easy to grasp". This book follows this dictum faithfully. Yashavant has crafted well thought out programming examples for every aspect of C Programming. Some of the highlighting features of the book are: Traditional C Programming: Pointers, Complete build process, Low-level File I/O, Structures, Unions, Bit-fields, Bitwise Operators, Creating Function Libraries; C Under Linux: Signals and Signal Handling; Blocking of Signals; Event Driven Programming; Process, PIDs, Zombies; Forking of Process; GNOME Programming Using GTK Library; C Under Windows: Windows Programming Model; Windows Messaging Architecture; Mouse Programming; Hardware Interaction; and Windows Hooks.

    But what is the mind? What do we mean when we say we are "aware" of something? What is this peculiar state in our heads, at once utterly familiar and bewilderingly mysterious, that we call awareness or consciousness? In Physics in Mind, eminent biophysicist Werner R. Loewenstein argues that to answer these questions, we must first understand the physical mechanisms that underlie the workings of the mind. And so begins an exhilarating journey along the sensory data stream of the brain, which shows how our most complex organ processes the vast amounts of information coming in through our senses to create a coherent, meaningful picture of the world. Bringing information theory to bear on recent advances in the neurosciences, Loewenstein reveals a web of immense computational power inside the brain. He introduces the revolutionary idea that quantum mechanics could be fundamental to how our minds almost instantaneously deal with staggering amounts of information, as in the case of the information streaming through our eyes. Combining cutting-edge research in neuroscience and physics, Loewenstein presents an ambitious hypothesis about the parallel processing of sensory information that is the heart, hub, and pivot of the cognitive brain. Wide-ranging and brimming with insight, Physics in Mind breaks new ground in our understanding of how the mind works.

    While Lean methodologies were developed in manufacturing environments, many Lean principles can be applied to any kind of business or activity; from software development to accounting systems to household chores.This short book introduces the core concepts of kanban and offers a step by step guide to getting started with kanban.

    But aren't you curious about what else the Web can do for you? Or if there are better, faster, or easier ways to do what you're already doing? Let the world's foremost technology writer, Mark Frauenfelder, help you unlock the Internet's potential--and open up a richer, nimbler, and more useful trove of resources and services, including:EXPRESS YOURSELF, SAFELY. Create and share blogs, podcasts, and online video with friends, family, and millions of potential audience members, while protecting yourself from identity theft and fraud.DIVIDE AND CONQUER. Tackle even the most complex online tasks with ease, from whipping up a gorgeous Web site to doing all your work faster and more efficiently within your browser, from word processing to investing to planning a party.THE RIGHT WAY, EVERY TIME. Master state-of-the-art techniques for doing everything from selling your house to shopping for electronics, with hundreds of carefully researched tips and tricks.TIPS FROM THE INSIDERS. Mark has asked dozens of the best bloggers around to share their favorite tips on getting the most out of the Web.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)