Books: 5

Julia

CoverTitleYear
Explore the world of data science from scratch with Julia by your side
KEY FEATURES * An in-depth exploration of Julia's growing ecosystem of packages * Work with the most powerful open-source libraries for deep learning, data wrangling, and data visualization * Learn about deep learning using Mocha.jl and give speed and high performance to data analysis on large data more » sets BOOK DESCRIPTION Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia. WHAT YOU WILL LEARN * Apply statistical models in Julia for data-driven decisions * Understanding the process of data munging and data preparation using Julia * Explore techniques to visualize data using Julia and D3 based packages * Using Julia to create self-learning systems using cutting edge machine learning algorithms * Create supervised and unsupervised machine learning systems using Julia. Also, explore ensemble models * Build a recommendation engine in Julia * Dive into Julia’s deep learning framework and build a system using Mocha.jl ABOUT THE AUTHOR Anshul Joshi is a data science professional with more than 2 years of experience primarily in data munging, recommendation systems, predictive modeling, and distributed computing. He is a deep learning and AI enthusiast. Most of the time, he can be caught exploring GitHub or trying anything new on which he can get his hands on. He blogs on anshuljoshi.xyz. TABLE OF CONTENTS 1. The Groundwork – Julia's Environment 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning « less
2016
Leverage the power of Julia to design and develop high performing programs
ABOUT THIS BOOK * Get to know the best techniques to create blazingly fast programs with Julia * Stand out from the crowd by developing code that runs faster than your peers' code * Complete an extensive data science project through the entire cycle from ETL to analytics and data visualization WHO more » THIS BOOK IS FOR This learning path is for data scientists and for all those who work in technical and scientific computation projects. It will be great for Julia developers who are interested in high-performance technical computing. This learning path assumes that you already have some basic working knowledge of Julia's syntax and high-level dynamic languages such as MATLAB, R, Python, or Ruby. WHAT YOU WILL LEARN * Set up your Julia environment to achieve the highest productivity * Solve your tasks in a high-level dynamic language and use types for your data only when needed * Apply Julia to tackle problems concurrently and in a distributed environment * Get a sense of the possibilities and limitations of Julia's performance * Use Julia arrays to write high performance code * Build a data science project through the entire cycle of ETL, analytics, and data visualization * Display graphics and visualizations to carry out modeling and simulation in Julia * Develop your own packages and contribute to the Julia Community IN DETAIL In this learning path, you will learn to use an interesting and dynamic programming language—Julia! You will get a chance to tackle your numerical and data problems with Julia. You'll begin the journey by setting up a running Julia platform before exploring its various built-in types. We'll then move on to the various functions and constructs in Julia. We'll walk through the two important collection types—arrays and matrices in Julia. You will dive into how Julia uses type information to achieve its performance goals, and how to use multiple dispatch to help the compiler emit high performance machine code. You will see how Julia's design makes code fast, and you'll see its distributed computing capabilities. By the end of this learning path, you will see how data works using simple statistics and analytics, and you'll discover its high and dynamic performance—its real strength, which makes it particularly useful in highly intensive computing tasks. This learning path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * Getting Started with Julia by Ivo Balvaert * Julia High Performance by Avik Sengupta * Mastering Julia by Malcolm Sherrington STYLE AND APPROACH This hands-on manual will give you great explanations of the important concepts related to Julia programming. « less
2016
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
ABOUT THIS BOOK * Design, engineer and deploy scalable machine learning solutions with the power of Python * Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework * Build state-of-the-art models and develop personalized recommendations to perform machine more » learning at scale WHO THIS BOOK IS FOR This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. WHAT YOU WILL LEARN * Apply the most scalable machine learning algorithms * Work with modern state-of-the-art large-scale machine learning techniques * Increase predictive accuracy with deep learning and scalable data-handling techniques * Improve your work by combining the MapReduce framework with Spark * Build powerful ensembles at scale * Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine IN DETAIL Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. STYLE AND APPROACH This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow. « less
2016
ABOUT THIS BOOK * Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark * Comprehensive practical solutions taking you into the future of machine learning * Go a step further and integrate your machine learning projects with more » Hadoop WHO THIS BOOK IS FOR This book has been created for data scientists who want to see Machine learning in action and explore its real-world applications. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. WHAT YOU WILL LEARN * Implement a wide range of algorithms and techniques for tackling complex data * Get to grips with some of the most powerful languages in data science, including R, Python, and Julia * Harness the capabilities of Spark and Mahout used in conjunction with Hadoop to manage and process data successfully * Apply the appropriate Machine learning technique to address a real-world problem * Get acquainted with deep learning and find out how neural networks are being used at the cutting edge of Machine learning * Explore the future of Machine learning and dive deeper into polyglot persistence, semantic data, and more IN DETAIL This book explores an extensive range of Machine learning techniques, uncovering hidden tips and tricks for several types of data using practical real-world examples. While Machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for modern data scientists who want to get to grips with Machine learning's real-world application. The book also explores cutting-edge advances in Machine learning, with worked examples and guidance on Deep learning and Reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced Machine learning methodologies. « less
2016
Enter the exciting world of Julia, a high-performance language for technical computing
Julia is a new open source programming language that is used in the field of data science computing. It was created to solve the dilemma between high-level slow code and fast but low-level code, and the necessity to use both to achieve high performance. This book will give you a head start to tackle more » your numerical and data problems with Julia. Your journey will begin by learning how to set up a running Julia platform before exploring its various built-in types. You will then move on to cover the different functions and constructs in Julia. The book will then walk you through the two important collection types - arrays and matrices. Over the course of the book, you will also be introduced to homoiconicity, the meta-programming concept in Julia. Towards the concluding part of the book, you will also learn how to run external programs. This book will cover all you need to know about Julia to leverage its high speed and efficiency. « less
2015