Search Results: practical-machine-learning-with-h2o-powerful-scalable-techniques-for-deep-learning-and-ai

Practical Machine Learning with H2O

Powerful, Scalable Techniques for Deep Learning and AI

Author: Darren Cook

Publisher: "O'Reilly Media, Inc."

ISBN: 1491964553

Category: Computers

Page: 300

View: 6270

Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

Practical Machine Learning with H2O

Powerful, Scalable Techniques for Deep Learning and AI

Author: Darren Cook

Publisher: "O'Reilly Media, Inc."

ISBN: 149196457X


Page: 300

View: 3695

Learn how to construct machine learning and data analysis scalable for big data using H2O software, using sample data sets and several machine-learning techniques including deep learning, random forests, unsupervised learning and ensemble learning.

R Deep Learning Essentials

Author: Dr. Joshua F. Wiley

Publisher: Packt Publishing Ltd

ISBN: 1785284711

Category: Computers

Page: 170

View: 2742

Build automatic classification and prediction models using unsupervised learning About This Book Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models Build models relating to neural networks, prediction and deep prediction Who This Book Is For This book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts. What You Will Learn Set up the R package H2O to train deep learning models Understand the core concepts behind deep learning models Use Autoencoders to identify anomalous data or outliers Predict or classify data automatically using deep neural networks Build generalizable models using regularization to avoid overfitting the training data In Detail Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models. Style and approach This book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.

Python Deep Learning

Author: Valentino Zocca,Gianmario Spacagna,Daniel Slater,Peter Roelants

Publisher: Packt Publishing Ltd

ISBN: 1786460661

Category: Computers

Page: 406

View: 7995

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more Who This Book Is For This book is for Data Science practitioners as well as aspirants who have a basic foundational understanding of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual understanding of calculus and statistics is also desired. What You Will Learn Get a practical deep dive into deep learning algorithms Explore deep learning further with Theano, Caffe, Keras, and TensorFlow Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders and Restricted Boltzmann Machines Dive into Deep Belief Nets and Deep Neural Networks Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Get to know device strategies so you can use deep learning algorithms and libraries in the real world In Detail With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you'll find everything inside. Style and approach Python Machine Learning by example follows practical hands on approach. It walks you through the key elements of Python and its powerful machine learning libraries with the help of real world projects.

Large Scale Machine Learning with Python

Author: Bastiaan Sjardin,Luca Massaron,Alberto Boschetti

Publisher: Packt Publishing Ltd

ISBN: 1785888021

Category: Computers

Page: 420

View: 1837

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 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.

Practical Data Science Cookbook

Author: Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta

Publisher: Packt Publishing Ltd

ISBN: 178712326X

Category: Computers

Page: 434

View: 9022

Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization

Neural Network Methods in Natural Language Processing

Author: Yoav Goldberg

Publisher: Morgan & Claypool Publishers

ISBN: 162705295X

Category: Computers

Page: 309

View: 3163

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.

Learning Angular

A Hands-On Guide to Angular 2 and Angular 4

Author: Brad Dayley,Brendan Dayley,Caleb Dayley

Publisher: Addison-Wesley Professional

ISBN: 0134577027

Category: Computers

Page: 240

View: 8919

Learning Angular, Second Edition A Hands-On Guide to Angular 2 and Angular 4 Learning Angular teaches modern application development with Angular 2 and Angular 4. It begins with the basics of Angular and the technologies and techniques used throughout the book, such as key features of TypeScript, newer ES6 syntax and concepts, and details about the tools needed to write professional Angular applications. The reader will next build an Angular application from scratch while learning about the primary pieces of an Angular application and see how they work together. Using lots of examples, the core parts of Angular will be introduced, such as Components, the Router, and Services. The book also covers techniques like server-side rendering and how to incrementally add Angular 2+ features to existing AngularJS applications. Finally, the reader will gain insight into advanced skills that should be part of any professional Angular developer’s toolkit such as testing, tooling options, and performance tuning. Understand how Angular is organized and learn best practices for designing Angular applications Quickly build Angular templates with built-in directives that enhance the user experience Bind UI elements to your data model, so changes to your model and UI occur automatically in tandem Define custom Angular directives that extend HTML Implement zoomable images, expandable lists, and other rich UI components Implement client-side services that interact with web servers Build dynamic browser views to provide even richer user interaction Create custom services you can easily reuse Implement rich UI components as custom Angular directives Contents at a Glance Introduction What is Angular Why Use Angular Who this Book is For How to Use this Book Getting the Source Code Chapter 1: Jumping into JavaScript Setting Up a JavaScript Development Environment Defining Variables Understanding JavaScript Data Types Using Operators Implementing Looping Creating Functions Understanding Variable Scope Using JavaScript Objects Manipulating Strings Working with Arrays Adding Error Handling Chapter 2: Jumping into TypeScript Learning the Different Types Understanding Interfaces Implementing Classes Implementing Modules Understanding Functions Chapter 3: Getting Started with Angular Why Angular? Understanding Angular Separation of Responsibilities Adding Angular to Your Environment Using the Angular CLI Creating a Basic Angular Application Chapter 4: Angular Components Component Configuration Building the Template Using Constructors Using External Templates Implementing Directives Chapter 5: Expressions Using Expressions Using Pipes Building a Custom Pipe Chapter 6: Data Binding Understanding Data Binding Interpolation Property Binding Attribute Binding Class Binding Style Binding Event Binding Two-Way Binding Chapter 7: Built-in Directives Understanding Directives Using Built-in Directives Structural Directives Attribute Directives Chapter 8: Custom Directives Creating a Custom Attribute Directive Creating a Custom Directive with a Component Chapter 9: Events and Change Detection Using Browser Events Emitting Custom Events Using Observables Chapter 10: Implementing Angular Services in Web Applications Understanding Angular Services Using the Built in Services Sending HTTP GET and PUT Requests with the http Service Implementing a Simple Mock Server Using the http Service Changing Views with the router Service Implementing a Router with a Navigation Bar Implementing a Router with Parameters Chapter 11: Creating Your Own Custom Angular Service Integrating Custom Services into Angular Applications Implementing a Simple Application That Uses a Constant Data Service Implementing a Data Transform Service Implementing a Variable Data Service Implementing a Service that Returns a Promise Implementing a Shared Service Chapter 12: Having Fun with Angular Implementing an Angular Application That Uses the Animation Service Implementing an Angular Application That Zooms in on Images Implementing an Angular Application That Enables Drag and Drop Implementing a Star Rating Angular Component

Practical Convolutional Neural Networks

Implement advanced deep learning models using Python

Author: Mohit Sewak,Md. Rezaul Karim,Pradeep Pujari

Publisher: Packt Publishing Ltd

ISBN: 1788394143

Category: Computers

Page: 218

View: 6214

One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

Getting Started with TensorFlow

Author: Giancarlo Zaccone

Publisher: Packt Publishing Ltd

ISBN: 1786469065

Category: Computers

Page: 180

View: 5443

Get up and running with the latest numerical computing library by Google and dive deeper into your data! About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow! Dive into the next generation of numerical computing and get the most out of your data with this quick guide Who This Book Is For This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful. What You Will Learn Install and adopt TensorFlow in your Python environment to solve mathematical problems Get to know the basic machine and deep learning concepts Train and test neural networks to fit your data model Make predictions using regression algorithms Analyze your data with a clustering procedure Develop algorithms for clustering and data classification Use GPU computing to analyze big data In Detail Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application. Style and approach Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry—something you can connect to and will find familiar.

Mastering Machine Learning with Spark 2.x

Author: Alex Tellez,Max Pumperla,Michal Malohlava

Publisher: Packt Publishing Ltd

ISBN: 1785282417

Category: Computers

Page: 340

View: 9453

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorial About This Book Process and analyze big data in a distributed and scalable way Write sophisticated Spark pipelines that incorporate elaborate extraction Build and use regression models to predict flight delays Who This Book Is For Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark. What You Will Learn Use Spark streams to cluster tweets online Run the PageRank algorithm to compute user influence Perform complex manipulation of DataFrames using Spark Define Spark pipelines to compose individual data transformations Utilize generated models for off-line/on-line prediction Transfer the learning from an ensemble to a simpler Neural Network Understand basic graph properties and important graph operations Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language Use K-means algorithm to cluster movie reviews dataset In Detail The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment. Style and approach This book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.

Mastering Scala Machine Learning

Author: Alex Kozlov

Publisher: Packt Publishing Ltd

ISBN: 178588526X

Category: Computers

Page: 310

View: 9836

Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop About This Book This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability Who This Book Is For Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn Sharpen your functional programming skills in Scala using REPL Apply standard and advanced machine learning techniques using Scala Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala. Style and approach This hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques and libraries. This book provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

R Deep Learning Projects

Master the techniques to design and develop neural network models in R

Author: Yuxi (Hayden) Liu,Pablo Maldonado

Publisher: Packt Publishing Ltd

ISBN: 1788474554

Category: Computers

Page: N.A

View: 3140

5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn - Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec - Apply neural networks to perform handwritten digit recognition using MXNet - Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders -Master reconstructing images using variational autoencoders - Wade through sentiment analysis from movie reviews - Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks - Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

Machine Learning and Data Science

An Introduction to Statistical Learning Methods with R

Author: Daniel D. Gutierrez

Publisher: Technics Publications

ISBN: 1634620984

Category: Computers

Page: 282

View: 3720

A practitioner’s tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field – recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.

Natural Language Annotation for Machine Learning

A Guide to Corpus-Building for Applications

Author: James Pustejovsky,Amber Stubbs

Publisher: "O'Reilly Media, Inc."

ISBN: 1449359760

Category: Computers

Page: 342

View: 2375

Create your own natural language training corpus for machine learning. Whether you’re working with English, Chinese, or any other natural language, this hands-on book guides you through a proven annotation development cycle—the process of adding metadata to your training corpus to help ML algorithms work more efficiently. You don’t need any programming or linguistics experience to get started. Using detailed examples at every step, you’ll learn how the MATTER Annotation Development Process helps you Model, Annotate, Train, Test, Evaluate, and Revise your training corpus. You also get a complete walkthrough of a real-world annotation project. Define a clear annotation goal before collecting your dataset (corpus) Learn tools for analyzing the linguistic content of your corpus Build a model and specification for your annotation project Examine the different annotation formats, from basic XML to the Linguistic Annotation Framework Create a gold standard corpus that can be used to train and test ML algorithms Select the ML algorithms that will process your annotated data Evaluate the test results and revise your annotation task Learn how to use lightweight software for annotating texts and adjudicating the annotations This book is a perfect companion to O’Reilly’s Natural Language Processing with Python.

Building Machine Learning Projects with TensorFlow

Author: Rodolfo Bonnin

Publisher: Packt Publishing Ltd

ISBN: 1786466821

Category: Computers

Page: 282

View: 2643

Engaging projects that will teach you how complex data can be exploited to gain the most insight About This Book Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production. This example-rich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow It is a practical and methodically explained guide that allows you to apply Tensorflow's features from the very beginning. Who This Book Is For This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected. What You Will Learn Load, interact, dissect, process, and save complex datasets Solve classification and regression problems using state of the art techniques Predict the outcome of a simple time series using Linear Regression modeling Use a Logistic Regression scheme to predict the future result of a time series Classify images using deep neural network schemes Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer Resolve character recognition problems using the Recurrent Neural Network (RNN) model In Detail This book of projects highlights how TensorFlow can be used in different scenarios - this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production. Style and approach This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine learning and numerical computation – a must-have for your bookshelf!

R Data Structures and Algorithms

Author: Dr. PKS Prakash,Achyutuni Sri Krishna Rao

Publisher: Packt Publishing Ltd

ISBN: 1786464160

Category: Computers

Page: 276

View: 6231

Increase speed and performance of your applications with efficient data structures and algorithms About This Book See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples Find out about important and advanced data structures such as searching and sorting algorithms Understand important concepts such as big-o notation, dynamic programming, and functional data structured Who This Book Is For This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected. What You Will Learn Understand the rationality behind data structures and algorithms Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis Get to know the fundamentals of arrays and linked-based data structures Analyze types of sorting algorithms Search algorithms along with hashing Understand linear and tree-based indexing Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm Understand dynamic programming (Knapsack) and randomized algorithms In Detail In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. Style and approach This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples.

Pediatric Cardiology Board Review

Author: Benjamin W. Eidem,Bryan C. Cannon,Jonathan N. Johnson,Anthony C. Chang,Frank Cetta

Publisher: Lippincott Williams & Wilkins

ISBN: 149635124X

Category: Medical

Page: 326

View: 7725

More than 900 board-style questions prepare you for certification and recertification! Based on the popular Mayo Clinic Pediatric Cardiology Review course, this highly regarded testing resource provides easy access to more than 900 questions and answers on every aspect of pediatric cardiology. Full explanations are provided for every question, helping you focus your areas for review and make the most of your study time.

Introduction to Deep Learning Using R

A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

Author: Taweh Beysolow II

Publisher: Apress

ISBN: 1484227344

Category: Business & Economics

Page: 227

View: 3017

Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.

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