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Author: Zacharias Voulgaris Publisher: ISBN: 9781634628136 Category : Computers Languages : en Pages : 298
Book Description
Unleash 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. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia. The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julia's Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapter's material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which aren't in any of the existing packages.
Author: Zacharias Voulgaris Publisher: ISBN: 9781634628136 Category : Computers Languages : en Pages : 298
Book Description
Unleash 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. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia. The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julia's Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapter's material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which aren't in any of the existing packages.
Author: Tanmay Bakshi Publisher: McGraw Hill Professional ISBN: 1260456641 Category : Technology & Engineering Languages : en Pages : 191
Book Description
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A quick guide to start writing your own fun and useful Julia apps—no prior experience required! This engaging guide shows, step by step, how to build custom programs using Julia, the open-source, intuitive scripting language. Written by 15-year-old technology phenom Tanmay Bakshi, the book is presented in an accessible style that makes learning easy and enjoyable. Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages clearly explains the basics of Julia programming and takes a look at cutting-edge machine learning applications. You will also discover how to interface your Julia apps with code written in Python. Inside, you’ll learn to: • Set up and configure your Julia environment • Get up and running writing your own Julia apps • Define variables and use them in your programs • Use conditions, iterations, for-loops, and while-loops • Create, go through, and modify arrays • Build an app to manage things you lend and get back from your friends • Create and utilize dictionaries • Simplify maintenance of your code using functions • Apply functions on arrays and use functions recursively and generically • Understand and program basic machine learning apps
Author: Emil Hvitfeldt Publisher: CRC Press ISBN: 1000461971 Category : Computers Languages : en Pages : 402
Book Description
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
Author: Adrian Salceanu Publisher: Packt Publishing Ltd ISBN: 1788297253 Category : Computers Languages : en Pages : 494
Book Description
A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key FeaturesWork with powerful open-source libraries for data wrangling, analysis, and visualizationDevelop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with JuliaBook Description Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia. What you will learnLeverage Julia's strengths, its top packages, and main IDE optionsAnalyze and manipulate datasets using Julia and DataFramesWrite complex code while building real-life Julia applicationsDevelop and run a web app using Julia and the HTTP packageBuild a recommender system using supervised machine learning Perform exploratory data analysis Apply unsupervised machine learning algorithmsPerform time series data analysis, visualization, and forecastingWho this book is for Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.
Author: Dmitrijs Cudihins Publisher: Packt Publishing Ltd ISBN: 1788999231 Category : Computers Languages : en Pages : 192
Book Description
Explore the various packages in Julia that support image processing and build neural networks for video processing and object tracking. Key Features Build a full-fledged image processing application using JuliaImages Perform basic to advanced image and video stream processing with Julia's APIs Understand and optimize various features of OpenCV with easy examples Book Description Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it’s easy to use and lets you write easy-to-compile and efficient machine code. . This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You’ll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you’ll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who this book is for Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.
Author: Ben Lauwens Publisher: "O'Reilly Media, Inc." ISBN: 1492044989 Category : Computers Languages : en Pages : 301
Book Description
If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This 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. Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand types, methods, and multiple dispatch Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design and data structures through case studies
Author: Sandeep Nagar Publisher: Apress ISBN: 1484231716 Category : Computers Languages : en Pages : 359
Book Description
Get started with Julia for engineering and numerical computing, especially data science, machine learning, and scientific computing applications. This book explains how Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, MATLAB, SAS, or Stata combined with the speed, capacity, and performance of C, C++, or Java. You’ll learn the OOP principles required to get you started, then how to do basic mathematics with Julia. Other core functionality of Julia that you’ll cover, includes working with complex numbers, rational and irrational numbers, rings, and fields. Beginning Julia Programming takes you beyond these basics to harness Julia’s powerful features for mathematical functions in Julia, arrays for matrix operations, plotting, and more. Along the way, you also learn how to manage strings, write functions, work with control flows, and carry out I/O to implement and leverage the mathematics needed for your data science and analysis projects. "Julia walks like Python and runs like C". This phrase explains why Julia is quickly growing as the most favored option for data analytics and numerical computation. After reading and using this book, you'll have the essential knowledge and skills to build your first Julia-based application. What You'll Learn Obtain core skills in Julia Apply Julia in engineering and science applications Work with mathematical functions in Julia Use arrays, strings, functions, control flow, and I/O in Julia Carry out plotting and display basic graphics Who This Book Is For Those who are new to Julia; experienced users may also find this helpful as a reference.
Author: Zacharias Voulgaris Publisher: ISBN: 9781634621304 Category : Application software Languages : en Pages : 0
Book Description
After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function. Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia Options for Julia IDEs Programming structures and functions Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing Data visualization and some simple yet powerful statistics for data exploration purposes Dimensionality reduction and feature evaluation Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines) Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights. Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.
Author: Julia Cook Publisher: National Center for Youth Issues ISBN: 1937870898 Category : Juvenile Fiction Languages : en Pages : 33
Book Description
"My stomach feels like it's tied up in a knot. My knees lock up, and my face feels hot. You know what I mean? I'm Wilma Jean, The Worry Machine." Anxiety is a subjective sense of worry, apprehension, and/or fear. It is considered to be the number one health problem in America. Although quite common, anxiety disorders in children are often misdiagnosed and overlooked. Everyone feels fear, worry and apprehension from time to time, but when these feelings prevent a person from doing what he/she wants and/or needs to do, anxiety becomes a disability. This fun and humorous book addresses the problem of anxiety in a way that relates to children of all ages. It offers creative strategies for parents and teachers to use that can lessen the severity of anxiety. The goal of the book is to give children the tools needed to feel more in control of their anxiety. For those worries that are not in anyone's control (i.e. the weather) a worry hat is introduced. A fun read for Wilmas of all ages! Includes a note to parents and educators with tips on dealing with an anxious child.
Author: Kevin P. Murphy Publisher: MIT Press ISBN: 0262369303 Category : Computers Languages : en Pages : 858
Book Description
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
Author: Zacharias Voulgaris
Author: Zacharias Voulgaris
Author: Tanmay Bakshi
Author: Emil Hvitfeldt
Author: Adrian Salceanu
Author: Dmitrijs Cudihins
Author: Ben Lauwens
Author: Sandeep Nagar
Author: Zacharias Voulgaris
Author: Julia Cook
Author: Kevin P. Murphy