";s:4:"text";s:20295:"This book introduces machine learning methods in finance. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found inside â Page 1Once youâve mastered these techniques, youâll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Found inside â Page iIf you already know the basics of Node.js, now is the time to discover how to bring it to production level by leveraging its vast ecosystem of packages.With this book, you'll work with a varied collection of standards and frameworks and see ... Found insideWith this handbook, youâll 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 ... Presents case studies and instructions on how to solve data analysis problems using Python. Found inside â Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideThis book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... Found insideWritten by a GitHub engineer, this book is packed with insight on how GitHub works and how you can use it to become a more effective, efficient, and valuable member of any collaborative programming team. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, youâll have the solid foundation you need to start a career in data science. Found insideBy learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information thatâs initially too messy or difficult to access. The first of these is an image recognition application with TensorFlow â embracing the importance today of AI in your data analysis. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. Found inside â Page iThis book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Found insideWhat you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement ... With the adoption of machine learning in upcoming security products, itâs important for pentesters and security researchers to understand how these systems work, and to breach them for . About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. The book is based on Jannes Klaas' experience of running machine learning training courses for financial professionals. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. 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 ... Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. "This book is not just about learning to program; although you will learn to code. Found insideIf you have Python experience, this book shows you how to take advantage of the creative freedom Flask provides. Found insideIn the first half of the book, youâll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. Found insideThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. 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 insideThis book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. Found insideBecome an efficient data scientist with R About This Book Explore the R language from basic types and data structures to advanced topics Learn how to tackle programming problems and explore both functional and object-oriented programming ... Found inside â Page iThis new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. Found insideThis book is about making machine learning models and their decisions interpretable. Do you want to use R to tell stories? This book was written for youâwhether you already know some R or have never coded before. Most R texts focus only on programming or statistical theory. An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. 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. Found inside â 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. Found insideIn this book, youâll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Found inside â Page iAfter reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. Found inside â Page iThis book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Found inside â Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. 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 book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. This book covers: Python data model: understand how special methods are the key to the consistent behavior of objects Data structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode age ... This book also walks experienced JavaScript developers through modern module formats, how to namespace code effectively, and other essential topics. This book has fundamental theoretical and practical aspects of data analysis, useful for beginners and experienced researchers that are looking for a recipe or an analysis approach. Statistics and financial time series referred to throughout the book is based on Jannes Klaas ' experience running. Science tools and algorithms work by implementing them from scratch have the foundation! ; although you will analyze an image dataset many of the most fundamental data science hands-on approach appeal. A career in data science ; the hands-on approach will appeal to students from a range disciplines... 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