Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Found insideUnleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julia’s ability to run algorithms at lightning speed. Found inside – Page iIn this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. Found insideGet started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform ... In this book, we will explore some of the many features of SAS Visual Data Mining and Machine Learning including: programming in the Python interface; new, advanced data mining and machine learning procedures; pipeline building in Model ... Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found inside – Page iWritten by experienced hedge fund manager Andreas Clenow, this book provides a comprehensive insight into the strategies behind the booming trend following futures industry from the perspective of a market participant. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. There’s just one problem: distributed tracing can be hard. But it doesn’t have to be. With this practical guide, you’ll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Diving deep into the JavaScript language to show you how to write beautiful, effective code, this book uses extensive examples and immerses you in code from the start, while exercises and full-chapter projects give you hands-on experience ... Found insideTowards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book. Style and approach This comprehensive practical guide will teach you how to work with the Jupyter Notebook system. In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization ... This book will be a handy guide to quickly learn pandas and understand how it can empower you in the exciting world of data manipulation, analysis, and data science. Presents case studies and instructions on how to solve data analysis problems using Python. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. The text includes exercises and extensive references for further study, and a companion website with source code for all examples and additional debugging resources. The book will take you on a journey through the evolution of data analysis explaining each step in the process in a very simple and easy to understand manner. Found insideThe book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Found inside – Page iThis book draws upon author Moshe Zadka's years of Dev Ops experience and focuses on the parts of Python, and the Python ecosystem, that are relevant for DevOps engineers. Found insideThe definitive guide for statisticians and data scientists who understand the advantages of becoming proficient in both R and Python The first book of its kind, Python for R Users: A Data Science Approach makes it easy for R programmers to ... Found insideF. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer ... Found inside – Page 174To find this out , we can ask for the directory of a list using the dir ... have a Python interpreter such as Jupyter Notebook , you have the ability ... Found insideWith this practical guide, you'll learn how to conduct analytics on data where it lives, whether it's Hive, Cassandra, a relational database, or a proprietary data store. This book provides an introduction to artificial intelligence (AI) and deep learning (DL), IBM PowerAI, and components of IBM PowerAI, deploying IBM PowerAI, guidelines for working with data and creating models, an introduction to IBM ... Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Found insideYou can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. This practical guide quickly gets you up to speed on the details, best practices, and pitfalls of using HDF5 to archive and share numerical datasets ranging in size from gigabytes to terabytes. Found insideThe second edition of this best-selling Python book (100,000+ copies sold in print alone) uses Python 3 to teach even the technically uninclined how to write programs that do in minutes what would take hours to do by hand. Found insideBuild text-based programs, including a number of simple games Learn how to re-use code by building your own modules Use Python's built-in data structures and packages to represent and make use of complex data from the Internet Who This Book ... Found insideStep-by-step tutorials on deep learning neural networks for computer vision in python with Keras. Found insideThis hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. This is a highly practical book, where every aspect is explained, all source code shown and no holds barred. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. Found insideIdeal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for ... Your one-stop guide to building an efficient data science pipeline using JupyterAbout This Book* Get the most out of your Jupyter notebook to complete the trickiest of tasks in Data Science* Learn all the tasks in the data science pipeline ... Found inside – Page iAfter reading this book, readers will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain ... This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. Found inside – Page iiIncluding numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, computer science, multimedia, and musicology. The book consists of eight chapters. Found insideThis groundbreaking work offers a first-of-its-kind overview of legal informatics, the academic discipline underlying the technological transformation and economics of the legal industry. Indispensable reference manual, from the creator of Mathematica. Found insideToday 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. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. "This book by Lisa Tauxe and others is a marvelous tool for education and research in Paleomagnetism. Found insideIn this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ... This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. 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