Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. 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.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse scikit-learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural netsApply practical code examples without acquiring excessive machine learning theory or algorithm details
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.
TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.What you will learnSet up the TensorFlow environment for deep learningConstruct your own ConvNets for effective image processingUse LSTMs for image caption generationForecast stock prediction accurately with an LSTM architectureLearn what semantic matching is by detecting duplicate Quora questionsSet up an AWS instance with TensorFlow to train GANsTrain and set up a chatbot to understand and interpret human inputBuild an AI capable of playing a video game by itself –and win it!Who this book is for
This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.What You Will LearnUnderstand the key frameworks in data science, machine learning, and deep learningHarness the power of the latest Python open source libraries in machine learningExplore machine learning techniques using challenging real-world dataMaster deep neural network implementation using the TensorFlow libraryLearn the mechanics of classification algorithms to implement the best tool for the jobPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringDelve deeper into textual and social media data using sentiment analysisIn Detail
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.
If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.Style and Approach
Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.
The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.Learn how to apply the tidy text format to NLPUse sentiment analysis to mine the emotional content of textIdentify a document’s most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R’s tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages
This book has a companion website: http://doi.org/10.1075/z.195.website
You will learn how to get the most from the XLIFF standard, use best practices in your translation workflow, extend XLIFF, and use the XLIFF modules.
This book is for localization coordinators, technical writers, content management system vendors, localization service providers, and consultants who want to incorporate XLIFF into their customers' publishing workflow.
Getting Started introduces XLIFF, the translation process, and the major parts of XLIFF, including the core and modules.
Applied XLIFF describes how XLIFF supports translation of XML (including DITA), websites, office documents, graphics, and software user interfaces.
XLIFF Core: introduces the XLIFF core features, including:Preserving document structure Marking up text for translation Segmentation and sub-flows Fragment identification Extensibility
XLIFF Modules: introduces the XLIFF modules, including:Translation Candidates Glossary Format Style Metadata Resource Data Change Tracking Size and Length Restriction Validation
XLIFF 2.0 Structure: describes the XLIFF 2.0 schemas, including the core structural and inline elements and module schemas.
Examples: detailed examples, including XSL code, for transforming XML source files into and out of XLIFF.
This book is for Go developers who are familiar with the Go syntax and can develop, build, and run basic Go programs. If you want to explore the field of machine learning and you love Go, then this book is for you! Machine Learning with Go will give readers the practical skills to perform the most common machine learning tasks with Go. Familiarity with some statistics and math topics is necessary.What You Will LearnLearn about data gathering, organization, parsing, and cleaning.Explore matrices, linear algebra, statistics, and probability.See how to evaluate and validate models.Look at regression, classification, clustering.Learn about neural networks and deep learningUtilize times series models and anomaly detection.Get to grip with techniques for deploying and distributing analyses and models.Optimize machine learning workflow techniquesIn Detail
The mission of this book is to turn readers into productive, innovative data analysts who leverage Go to build robust and valuable applications. To this end, the book clearly introduces the technical aspects of building predictive models in Go, but it also helps the reader understand how machine learning workflows are being applied in real-world scenarios.
Machine Learning with Go shows readers how to be productive in machine learning while also producing applications that maintain a high level of integrity. It also gives readers patterns to overcome challenges that are often encountered when trying to integrate machine learning in an engineering organization.
The readers will begin by gaining a solid understanding of how to gather, organize, and parse real-work data from a variety of sources. Readers will then develop a solid statistical toolkit that will allow them to quickly understand gain intuition about the content of a dataset. Finally, the readers will gain hands-on experience implementing essential machine learning techniques (regression, classification, clustering, and so on) with the relevant Go packages.
Finally, the reader will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.Style and approach
This book connects the fundamental, theoretical concepts behind Machine Learning to practical implementations using the Go programming language.
Starting with an overview of research in the field of corpus linguistics and language teaching, various scenarios including academic and professional settings, as well as English as International Language, are described. Corpus-Based Approaches to ELT goes on to put forward several chapters focusing on error analysis using learner corpora and comparable native speaker corpora. Some of these chapters use translations and their original sources, while others compare the production of learners from different L1 in multilingual learner corpora. Also presented are new tools for corpus processing: a query program for parallel corpora, and the provision of tools to implement pedagogical annotation. The last section discuss the challenges and opportunities that multilayered and multimodal corpora may pose to corpus linguistic investigation.
This book will be indispensible to those teaching in higher education and wishing to develop corpus-based approaches, as well as researchers in the field of English Language Teaching.
Text is a common fabric of society, yet it is still challenging for our technology to make sense of text. This is where taxonomies can help. In this book, legendary Bill Inmon will introduce you to the concept of taxonomies and how they are used to simplify and understand text. We emphasize the practical aspects of taxonomies, and the subsequent usage of taxonomies as a basis for textual analytics.
This book is for managers who have to deal with text, students of computer science, programmers who need to understand taxonomies, systems analysts who hope to draw business value out of a body of text, and especially those who are struggling to decode data lakes. Hopefully for those individuals (and many more), this book will serve as both an introduction to taxonomies and a guide to how taxonomies can be used to bring text into the realm of corporate decision-making.
This book will introduce you to the world of taxonomies, as well as explore:Simple and complex taxonomies Ontologies Obtaining taxonomies Changing taxonomies Taxonomies and data models Types of textual data Textual analytics.
In addition, several case studies are presented from industries as diverse as banking, call centers, and travel.
Perl has a strong history of automated tests. A very early release of Perl 1.0 included a comprehensive test suite, and it's only improved from there. Learning how Perl's test tools work and how to put them together to solve all sorts of previously intractable problems can make you a better programmer in general. Besides, it's easy to use the Perl tools described to handle all sorts of testing problems that you may encounter, even in other languages.
Like all titles in O'Reilly's Developer's Notebook series, this "all lab, no lecture" book skips the boring prose and focuses instead on a series of exercises that speak to you instead of at you.
Perl Testing: A Developer's Notebook will help you dive right in and:Write basic Perl tests with ease and interpret the resultsApply special techniques and modules to improve your testsBundle test suites along with projectsTest databases and their dataTest websites and web projectsUse the "Test Anything Protocol" which tests projects written in languages other than Perl
With today's increased workloads and short development cycles, unit tests are more vital to building robust, high-quality software than ever before. Once mastered, these lessons will help you ensure low-level code correctness, reduce software development cycle time, and ease maintenance burdens.
You don't have to be a die-hard free and open source software developer who lives, breathes, and dreams Perl to use this book. You just have to want to do your job a little bit better.
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 corpusBuild a model and specification for your annotation projectExamine the different annotation formats, from basic XML to the Linguistic Annotation FrameworkCreate a gold standard corpus that can be used to train and test ML algorithmsSelect the ML algorithms that will process your annotated dataEvaluate the test results and revise your annotation taskLearn 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.
You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text.
By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.
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.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you’ll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills
Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText.
This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification.
Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch.
Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects.What you will learnCreate models using the default command line options in fastTextUnderstand the algorithms used in fastText to create word vectorsCombine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipelineExplore word representation and sentence classification using fastTextUse Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficientlyDevelop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorchWho this book is for
This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.
The mathematical background needed to understand this book is similar to what is necessary to read other textbooks on geometric design; most of it is basic linear algebra and analysis. More advanced mathematical material is introduced using elementary explanations. Reading Geometric Continuity of Curves and Surfaces provides an excellent opportunity to recall and exercise necessary mathematical notions and it may be your next step towards better practice and higher understanding of design principles.
Natural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more. The second edition of Natural Language Processing with Java teaches you how to perform language analysis with the help of Java libraries, while constantly gaining insights from the outcomes.
You’ll start by understanding how NLP and its various concepts work. Having got to grips with the basics, you’ll explore important tools and libraries in Java for NLP, such as CoreNLP, OpenNLP, Neuroph, and Mallet. You’ll then start performing NLP on different inputs and tasks, such as tokenization, model training, parts-of-speech and parsing trees. You’ll learn about statistical machine translation, summarization, dialog systems, complex searches, supervised and unsupervised NLP, and more.
By the end of this book, you’ll have learned more about NLP, neural networks, and various other trained models in Java for enhancing the performance of NLP applications.What you will learnUnderstand basic NLP tasks and how they relate to one anotherDiscover and use the available tokenization enginesApply search techniques to find people, as well as things, within a documentConstruct solutions to identify parts of speech within sentencesUse parsers to extract relationships between elements of a documentIdentify topics in a set of documentsExplore topic modeling from a documentWho this book is for
Natural Language Processing with Java is for you if you are a data analyst, data scientist, or machine learning engineer who wants to extract information from a language using Java. Knowledge of Java programming is needed, while a basic understanding of statistics will be useful but not mandatory.
A must-read for anyone involved in the purchase, design or study of identity matching systems, this book describes how linguistic and onomastic knowledge can be used to create a more reliable and precise identity search.
This book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.What You Will LearnImplement string matching algorithms and normalization techniquesImplement statistical language modeling techniquesGet an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generatorDevelop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n gram approachFamiliarize yourself with concepts such as the Treebank construct, CFG construction, the CYK Chart Parsing algorithm, and the Earley Chart Parsing algorithmDevelop an NER-based system and understand and apply the concepts of sentiment analysisUnderstand and implement the concepts of Information Retrieval and text summarizationDevelop a Discourse Analysis System and Anaphora Resolution based systemIn Detail
Natural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.
This book will give you expertise on how to employ various NLP tasks in Python, giving you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and assist you in creating your own NLP projects using NLTK.
You will sequentially be guided through applying machine learning tools to develop various models. We'll give you clarity on how to create training data and how to implement major NLP applications such as Named Entity Recognition, Question Answering System, Discourse Analysis, Transliteration, Word Sense disambiguation, Information Retrieval, Sentiment Analysis, Text Summarization, and Anaphora Resolution.Style and approach
This is an easy-to-follow guide, full of hands-on examples of real-world tasks. Each topic is explained and placed in context, and for the more inquisitive, there are more details of the concepts used.
* dispersion plots
* building and annotating corpora
Illustrated with a number of real-life examples of corpus-based DA from a range of sources and covering a variety of subjects, this is an informative introduction to using corpus linguistics as a methodology in discourse analysis.
But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.
With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.
They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on howsoftware ought to be created.
Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.
Praise for Perl Best Practices from Perl community members:
"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide."-- Randal Schwartz
"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book."-- Peter Scott
"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too."-- Andy Lester
"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years."-- Bill Odom
"Finally, a means to bring lasting order to the process and product of real Perl development teams."-- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable Perl, and is a definitive citation source when coaching other programmers."-- Bennett Todd"I've been teaching Perl for years, and find the same question keeps being asked: Where can I find a reference for writing reusable, maintainable Perl code? Finally I have a decent answer."-- Paul Fenwick"At last a well researched, well thought-out, comprehensive guide to Perl style. Instead of each of us developing our own, we can learn good practices from one of Perl's most prolific and experienced authors. I recommend this book to anyone who prefers getting on with the job rather than going back and fixing errors caused by syntax and poor style issues."-- Jacinta Richardson"If you care about programming in any language read this book. Even if you don't intend to follow all of the practices, thinking through your style will improve it."-- Steven Lembark"The Perl community's best author is back with another outstanding book. There has never been a comprehensive reference on high quality Perl coding and style until Perl Best Practices. This book fills a large gap in every Perl bookshelf."-- Uri Guttman
Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.What You Will LearnBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet.Analyzing a Deep boltzmann machineSetting up and Analysing Deep belief networksBuilding supervised model using various machine learning algorithmsSet up variants of basic convolution functionRepresent data using Autoencoders.Explore generative models available in Deep Learning.Discover sequence modeling using Recurrent netsLearn fundamentals of Reinforcement LeaningLearn the steps involved in applying Deep Learning in text miningExplore application of deep learning in signal processingUtilize Transfer learning for utilizing pre-trained modelTrain a deep learning model on a GPUIn Detail
Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.
This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.
By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.Style and approach
Collection of hands-on recipes that would act as your all-time reference for your deep learning needs
Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.
This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.
By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learnBuild a prediction model using decision trees and random forestUse neural networks, decision trees, and random forests for classificationDetect YouTube comment spam with a bag-of-words and random forestsIdentify handwritten mathematical symbols with convolutional neural networksRevise the bird species identifier to use imagesLearn to detect positive and negative sentiment in user reviewsWho this book is for
Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code
On the surface level, Panini has defined rules in a forward looking generative fashion which makes reverse analysis necessary for parsing. Since parsing inflections is the first basic step towards complete analysis, the present work has relevance for any larger system that may evolve in future.
Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.
This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.
You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.
This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasetsLearn how to pre-process and clean textual dataConvert textual data into vector space representationsUsing spaCy to process text Train your own NLP models for computational linguisticsUse statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learnEmploy deep learning techniques for text analysis using KerasWho this book is for
This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.
Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbotsWho this book is for
Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
This book is intended for Python developers who wish to start with natural language processing and want to make their applications smarter by implementing NLP in them.What You Will LearnFocus on Python programming paradigms, which are used to develop NLP applicationsUnderstand corpus analysis and different types of data attribute.Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so onLearn about Features Extraction and Feature selection as part of Features Engineering.Explore the advantages of vectorization in Deep Learning.Get a better understanding of the architecture of a rule-based system.Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems.Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems.In Detail
This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them.
During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis.
You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data.
By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.Style and approach
This book teaches the readers various aspects of natural language Processing using NLTK. It takes the reader from the basic to advance level in a smooth way.
For upper-level undergraduate students and graduate students in theoretical linguistics, computer-science students with interests in computational linguistics, logic programming and artificial intelligence, mathematicians and logicians with interests in linguistics and the semantics of natural language.
This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. The book presents a state-of-the-art overview of current research in parsing tehcnologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing.
This book is the fourth in a line of such collections, and its breadth over coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. It will also be of interest to designers, developers, and advanced users of nautral language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.
Providing a probabilistic framework for examining the ways in which persistence - among several other internal and external factors - influences speakers' linguistic choices, the book departs from most writings in the field in that it seeks to bridge several research traditions. While it is concerned, in a classically variationist spirit, with internal and external determinants of grammatical variation in English, it also draws heavily on ideas and evidence developed by psycholinguists and discourse analysts. In seeking to construct a comprehensive model of how speakers make linguistic choices, the study ultimately contributes to a theory of how spoken language works.
The book is of interest to graduate students and researchers in variationist sociolinguistics, probabilistic linguistics, psycholinguistics, and computational linguistics.
Modeling Creativity examines creativity in a number of different perspectives: from its origins in nature, which is essentially blind, to humans and machines, and from generating creative ideas to evaluating and learning their novelty and usefulness. We will use a hands-on approach with case studies and examples in the Python programming language.
Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start.Apply TDD to write and run tests before you start codingLearn the best uses and tradeoffs of eight machine learning algorithmsUse real-world examples to test each algorithm through engaging, hands-on exercisesUnderstand the similarities between TDD and the scientific method for validating solutionsBe aware of the risks of machine learning, such as underfitting and overfitting dataExplore techniques for improving your machine-learning models or data extraction