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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation

Machine Learning - Master Machine Learning Fundamentals For Beginners (Paperback): M G Martin Machine Learning - Master Machine Learning Fundamentals For Beginners (Paperback)
M G Martin
R345 Discovery Miles 3 450 Ships in 10 - 15 working days
Deep Natural Language Processing and AI Applications for Industry 5.0 (Paperback): Poonam Tanwar, Arti Saxena, C. Priya Deep Natural Language Processing and AI Applications for Industry 5.0 (Paperback)
Poonam Tanwar, Arti Saxena, C. Priya
R5,647 Discovery Miles 56 470 Ships in 10 - 15 working days

To sustain and stay at the top of the market and give absolute comfort to the consumers, industries are using different strategies and technologies. Natural language processing (NLP) is a technology widely penetrating the market, irrespective of the industry and domains. It is extensively applied in businesses today, and it is the buzzword in every engineer's life. NLP can be implemented in all those areas where artificial intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has improved the imitation of professional translations over the years. When applied in neural machine translation, NLP helps educate neural machine networks. This can be used by industries to translate low-impact content including emails, regulatory texts, etc. Such machine translation tools speed up communication with partners while enriching other business interactions. Deep Natural Language Processing and AI Applications for Industry 5.0 provides innovative research on the latest findings, ideas, and applications in fields of interest that fall under the scope of NLP including computational linguistics, deep NLP, web analysis, sentiments analysis for business, and industry perspective. This book covers a wide range of topics such as deep learning, deepfakes, text mining, blockchain technology, and more, making it a crucial text for anyone interested in NLP and artificial intelligence, including academicians, researchers, professionals, industry experts, business analysts, data scientists, data analysts, healthcare system designers, intelligent system designers, practitioners, and students.

Advanced Natural Language Processing with TensorFlow 2 - Build effective real-world NLP applications using NER, RNNs, seq2seq... Advanced Natural Language Processing with TensorFlow 2 - Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more (Paperback)
Ashish Bansal
R1,189 Discovery Miles 11 890 Ships in 10 - 15 working days

One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks Key Features Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2 Explore applications like text generation, summarization, weakly supervised labelling and more Read cutting edge material with seminal papers provided in the GitHub repository with full working code Book DescriptionRecently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems. What you will learn Grasp important pre-steps in building NLP applications like POS tagging Use transfer and weakly supervised learning using libraries like Snorkel Do sentiment analysis using BERT Apply encoder-decoder NN architectures and beam search for summarizing texts Use Transformer models with attention to bring images and text together Build apps that generate captions and answer questions about images using custom Transformers Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models Who this book is forThis is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

Deep Learning with Keras from Scratch (Paperback): Benjamin Young Deep Learning with Keras from Scratch (Paperback)
Benjamin Young
R1,067 Discovery Miles 10 670 Ships in 10 - 15 working days
Hands-On Natural Language Processing with PyTorch 1.x - Build smart, AI-driven linguistic applications using deep learning and... Hands-On Natural Language Processing with PyTorch 1.x - Build smart, AI-driven linguistic applications using deep learning and NLP techniques (Paperback)
Thomas Dop
R1,087 Discovery Miles 10 870 Ships in 10 - 15 working days

Become a proficient NLP data scientist by developing deep learning models for NLP and extract valuable insights from structured and unstructured data Key Features Get to grips with word embeddings, semantics, labeling, and high-level word representations using practical examples Learn modern approaches to NLP and explore state-of-the-art NLP models using PyTorch Improve your NLP applications with innovative neural networks such as RNNs, LSTMs, and CNNs Book DescriptionIn the internet age, where an increasing volume of text data is generated daily from social media and other platforms, being able to make sense of that data is a crucial skill. With this book, you'll learn how to extract valuable insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch and using CUDA to accelerate the processing speed, you'll explore how the NLP architecture works with the help of practical examples. This PyTorch NLP book will guide you through core concepts such as word embeddings, CBOW, and tokenization in PyTorch. You'll then learn techniques for processing textual data and see how deep learning can be used for NLP tasks. The book demonstrates how to implement deep learning and neural network architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you'll learn how to build advanced NLP models, such as conversational chatbots. By the end of this book, you'll not only have understood the different NLP problems that can be solved using deep learning with PyTorch, but also be able to build models to solve them. What you will learn Use NLP techniques for understanding, processing, and generating text Understand PyTorch, its applications and how it can be used to build deep linguistic models Explore the wide variety of deep learning architectures for NLP Develop the skills you need to process and represent both structured and unstructured NLP data Become well-versed with state-of-the-art technologies and exciting new developments in the NLP domain Create chatbots using attention-based neural networks Who this book is forThis PyTorch book is for NLP developers, machine learning and deep learning developers, and anyone interested in building intelligent language applications using both traditional NLP approaches and deep learning architectures. If you're looking to adopt modern NLP techniques and models for your development projects, this book is for you. Working knowledge of Python programming, along with basic working knowledge of NLP tasks, is required.

Ghosts, Robots, Automatic Writing - An AI Study Level Guide: An AI Study Level Guide: An AI Study Level Guide (Paperback): Anne... Ghosts, Robots, Automatic Writing - An AI Study Level Guide: An AI Study Level Guide: An AI Study Level Guide (Paperback)
Anne Alexander
R198 Discovery Miles 1 980 Ships in 10 - 15 working days
English-German and German-English Dictionary for the Iron and Steel Industry (German, Paperback, Softcover Reprint of the... English-German and German-English Dictionary for the Iron and Steel Industry (German, Paperback, Softcover Reprint of the Original 1st 1955 ed.)
Eduard L Koehler, Alois Legat
R1,419 Discovery Miles 14 190 Ships in 10 - 15 working days

Noch niemals ist die Zusammenarbeit der deutschsprachigen mit der angelsachsischen Welt in technischen Fragen so eindringlich und umfassend gewesen wie in den Jahren seit dem Ende des zweiten Weltkrieges. Das unter dem Namen des MARSHALL-Plans bekannte Aufbauwerk des amerikanischen Volkes hat in seiner Durchfuhrung einen besonders verstarkten Schriftverkehr uber technische Einzelheiten mit der Notwendigkeit der deutsch-englischen UEbersetzung mit sich gebracht; das Berg-und Huttenwesen steht dabei mit in der vordersten Reihe der Gebiete, fur die eine solche Aufgabe erwachsen ist. Auch ist es heute fur den Ingenieur in den Planungsstellen, in den Betrieben, in den Statten der wissenschaftlichen Forschung oder im Patentwesen, aber ebenso bereits fur den Studenten der technischen Facher mehr denn je zur zwingenden Forderung geworden, das englisch geschriebene Fachschrifttum verfolgen zu koennen. Technisches Englisch ist nun bekanntlich eine Sprache, die in vielen Belangen uber einen anderen Wortschatz und eine andere Zuordnung von Begriff und Wort verfugt als das Englisch des sonstigen taglichen Lebens oder des schoengeistigen Schrifttums. Bedenkliche Missverstand- nisse koennen entstehen, wenn dieser Tatsache nicht Rechnung getragen wird. UEberdies ist die technische Sprache schnellehig wandelbar wie die Technik selbst; die Schwierigkeiten, die daraus entstehen, treten dem Benutzer der bisher erschienenen technischen Fachwoerterbucher immer wieder einmal entgegen.

Deep Learning With Python - 3 Books in 1: A Hands-On Guide for Beginners+A Comprehensive Guide Beyond The Basics+A... Deep Learning With Python - 3 Books in 1: A Hands-On Guide for Beginners+A Comprehensive Guide Beyond The Basics+A Comprehensive Guide for Experts (Paperback)
Travis Booth
R889 Discovery Miles 8 890 Ships in 10 - 15 working days
Hands-On Machine Learning with C++ - Build, train, and deploy end-to-end machine learning and deep learning pipelines... Hands-On Machine Learning with C++ - Build, train, and deploy end-to-end machine learning and deep learning pipelines (Paperback)
Kirill Kolodiazhnyi
R1,496 Discovery Miles 14 960 Ships in 10 - 15 working days

Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key Features Become familiar with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Book DescriptionC++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You'll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you'll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you'll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. What you will learn Explore how to load and preprocess various data types to suitable C++ data structures Employ key machine learning algorithms with various C++ libraries Understand the grid-search approach to find the best parameters for a machine learning model Implement an algorithm for filtering anomalies in user data using Gaussian distribution Improve collaborative filtering to deal with dynamic user preferences Use C++ libraries and APIs to manage model structures and parameters Implement a C++ program to solve image classification tasks with LeNet architecture Who this book is forYou will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.

Natural Language Processing for Global and Local Business (Paperback): Fatih Pinarbasi, M. Nurdan Taskiran Natural Language Processing for Global and Local Business (Paperback)
Fatih Pinarbasi, M. Nurdan Taskiran
R5,342 Discovery Miles 53 420 Ships in 10 - 15 working days

The concept of natural language processing has become one of the preferred methods to better understand consumers, especially in recent years when digital technologies and research methods have developed exponentially. It has become apparent that when responding to international consumers through multiple platforms and speaking in the same language in which the consumers express themselves, companies are improving their standings within the public sphere. Natural Language Processing for Global and Local Business provides research exploring the theoretical and practical phenomenon of natural language processing through different languages and platforms in terms of today's conditions. Featuring coverage on a broad range of topics such as computational linguistics, information engineering, and translation technology, this book is ideally designed for IT specialists, academics, researchers, students, and business professionals seeking current research on improving and understanding the consumer experience.

Java Deep Learning Cookbook - Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j... Java Deep Learning Cookbook - Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j (Paperback)
Rahul Raj
R1,160 Discovery Miles 11 600 Ships in 10 - 15 working days

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch Key Features Install and configure Deeplearning4j to implement deep learning models from scratch Explore recipes for developing, training, and fine-tuning your neural network models in Java Model neural networks using datasets containing images, text, and time-series data Book DescriptionJava is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) - the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java. What you will learn Perform data normalization and wrangling using DL4J Build deep neural networks using DL4J Implement CNNs to solve image classification problems Train autoencoders to solve anomaly detection problems using DL4J Perform benchmarking and optimization to improve your model's performance Implement reinforcement learning for real-world use cases using RL4J Leverage the capabilities of DL4J in distributed systems Who this book is forIf you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.

Codeless Deep Learning with KNIME - Build, train, and deploy various deep neural network architectures using KNIME Analytics... Codeless Deep Learning with KNIME - Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform (Paperback)
Kathrin Melcher, Rosaria Silipo
R1,435 Discovery Miles 14 350 Ships in 10 - 15 working days

Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions Key Features Become well-versed with KNIME Analytics Platform to perform codeless deep learning Design and build deep learning workflows quickly and more easily using the KNIME GUI Discover different deployment options without using a single line of code with KNIME Analytics Platform Book DescriptionKNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It'll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you'll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You'll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you'll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you'll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network. What you will learn Use various common nodes to transform your data into the right structure suitable for training a neural network Understand neural network techniques such as loss functions, backpropagation, and hyperparameters Prepare and encode data appropriately to feed it into the network Build and train a classic feedforward network Develop and optimize an autoencoder network for outlier detection Implement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examples Deploy a trained deep learning network on real-world data Who this book is forThis book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.

The Singularity - Building a Better Future (Paperback): Nishanth Mudkey The Singularity - Building a Better Future (Paperback)
Nishanth Mudkey
R1,188 R967 Discovery Miles 9 670 Save R221 (19%) Ships in 10 - 15 working days
Learn Machine Learning for Finance - The comprehensive quickstart guide to build 6-figures passive income with stock and day... Learn Machine Learning for Finance - The comprehensive quickstart guide to build 6-figures passive income with stock and day trading. Master as a pro Python, Scikit, TensorFlow and Keras in 7 days (Paperback)
Jason Test, Mark Broker
R510 Discovery Miles 5 100 Ships in 10 - 15 working days
Machine Learning - The Complete Step-By-Step Guide To Learning and Understanding Machine Learning From Beginners, Intermediate... Machine Learning - The Complete Step-By-Step Guide To Learning and Understanding Machine Learning From Beginners, Intermediate Advanced, To Expert Concepts and Techniques (Paperback)
Peter Bradley
R452 Discovery Miles 4 520 Ships in 10 - 15 working days
Hands-On Deep Learning with Go - A practical guide to building and implementing neural network models using Go (Paperback):... Hands-On Deep Learning with Go - A practical guide to building and implementing neural network models using Go (Paperback)
Gareth Seneque, Darrell Chua
R1,254 Discovery Miles 12 540 Ships in 10 - 15 working days

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding of deep learning using Golang Build complex neural network models using Go libraries and Gorgonia Take your deep learning model from design to deployment with this handy guide Book DescriptionGo is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems. What you will learn Explore the Go ecosystem of libraries and communities for deep learning Get to grips with Neural Networks, their history, and how they work Design and implement Deep Neural Networks in Go Get a strong foundation of concepts such as Backpropagation and Momentum Build Variational Autoencoders and Restricted Boltzmann Machines using Go Build models with CUDA and benchmark CPU and GPU models Who this book is forThis book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.

The Natural Language Processing Workshop - Confidently design and build your own NLP projects with this easy-to-understand... The Natural Language Processing Workshop - Confidently design and build your own NLP projects with this easy-to-understand practical guide (Paperback, 2nd edition)
Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, …
R1,105 Discovery Miles 11 050 Ships in 10 - 15 working days

Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data Key Features Get familiar with key natural language processing (NLP) concepts and terminology Explore the functionalities and features of popular NLP tools Learn how to use Python programming and third-party libraries to perform NLP tasks Book DescriptionDo you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text. What you will learn Obtain, verify, clean and transform text data into a correct format for use Use methods such as tokenization and stemming for text extraction Develop a classifier to classify comments in Wikipedia articles Collect data from open websites with the help of web scraping Train a model to detect topics in a set of documents using topic modeling Discover techniques to represent text as word and document vectors Who this book is forThis book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.

Deep Learning for Natural Language Processing - Solve your natural language processing problems with smart deep neural networks... Deep Learning for Natural Language Processing - Solve your natural language processing problems with smart deep neural networks (Paperback)
Karthiek Reddy Bokka, Shubhangi Hora, Tanuj Jain, Monicah Wambugu
R975 Discovery Miles 9 750 Ships in 10 - 15 working days

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain insights into the basic building blocks of natural language processing Learn how to select the best deep neural network to solve your NLP problems Explore convolutional and recurrent neural networks and long short-term memory networks Book DescriptionApplying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learn Understand various pre-processing techniques for deep learning problems Build a vector representation of text using word2vec and GloVe Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP Build a machine translation model in Keras Develop a text generation application using LSTM Build a trigger word detection application using an attention model Who this book is forIf you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must.

Hands-On Neural Networks - Learn how to build and train your first neural network model using Python (Paperback): Leonardo De... Hands-On Neural Networks - Learn how to build and train your first neural network model using Python (Paperback)
Leonardo De Marchi, Laura Mitchell
R1,043 Discovery Miles 10 430 Ships in 10 - 15 working days

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key Features Explore neural network architecture and understand how it functions Learn algorithms to solve common problems using back propagation and perceptrons Understand how to apply neural networks to applications with the help of useful illustrations Book DescriptionNeural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learn Learn how to train a network by using backpropagation Discover how to load and transform images for use in neural networks Study how neural networks can be applied to a varied set of applications Solve common challenges faced in neural network development Understand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) network Get up to speed with advanced and complex deep learning concepts like LSTMs and NLP Explore innovative algorithms like GANs and deep reinforcement learning Who this book is forIf you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

Python - 2 Books in 1: Basic Programming & Machine Learning - The Comprehensive Guide to Learn and Apply Python Programming... Python - 2 Books in 1: Basic Programming & Machine Learning - The Comprehensive Guide to Learn and Apply Python Programming Language Using Best Practices and Advanced Features. (Paperback)
Ethem Mining
R728 Discovery Miles 7 280 Ships in 10 - 15 working days
Hands-On Python Natural Language Processing - Explore tools and techniques to analyze and process text with a view to building... Hands-On Python Natural Language Processing - Explore tools and techniques to analyze and process text with a view to building real-world NLP applications (Paperback)
Aman Kedia, Mayank Rasu
R1,110 Discovery Miles 11 100 Ships in 9 - 15 working days

Get well-versed with traditional as well as modern natural language processing concepts and techniques Key Features Perform various NLP tasks to build linguistic applications using Python libraries Understand, analyze, and generate text to provide accurate results Interpret human language using various NLP concepts, methodologies, and tools Book DescriptionNatural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP. What you will learn Understand how NLP powers modern applications Explore key NLP techniques to build your natural language vocabulary Transform text data into mathematical data structures and learn how to improve text mining models Discover how various neural network architectures work with natural language data Get the hang of building sophisticated text processing models using machine learning and deep learning Check out state-of-the-art architectures that have revolutionized research in the NLP domain Who this book is forThis NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.

Advanced Deep Learning with TensorFlow 2 and Keras - Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and... Advanced Deep Learning with TensorFlow 2 and Keras - Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition (Paperback, 2nd Revised edition)
Rowel Atienza
R1,232 Discovery Miles 12 320 Ships in 10 - 15 working days

Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book DescriptionAdvanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models - autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is forThis is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.

Machine Learning with Python - Comprehensive Guide of Tips and Tricks of using Machine Learning Theories with Python... Machine Learning with Python - Comprehensive Guide of Tips and Tricks of using Machine Learning Theories with Python (Paperback)
Ethan Williams
R523 Discovery Miles 5 230 Ships in 10 - 15 working days
Generative Adversarial Networks with Industrial Use Cases - Learning How to Build Gan Applications for Retail, Healthcare,... Generative Adversarial Networks with Industrial Use Cases - Learning How to Build Gan Applications for Retail, Healthcare, Telecom, Media, Education, and Hrtech (Paperback)
Navin K Manaswi
R504 Discovery Miles 5 040 Ships in 10 - 15 working days
Natural Language Processing Fundamentals - Build intelligent applications that can interpret the human language to deliver... Natural Language Processing Fundamentals - Build intelligent applications that can interpret the human language to deliver impactful results (Paperback)
Sohom Ghosh, Dwight Gunning
R1,087 Discovery Miles 10 870 Ships in 10 - 15 working days

Use Python and NLTK (Natural Language Toolkit) to build out your own text classifiers and solve common NLP problems. Key Features Assimilate key NLP concepts and terminologies Explore popular NLP tools and techniques Gain practical experience using NLP in application code Book DescriptionIf NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language. What you will learn Obtain, verify, and clean data before transforming it into a correct format for use Perform data analysis and machine learning tasks using Python Understand the basics of computational linguistics Build models for general natural language processing tasks Evaluate the performance of a model with the right metrics Visualize, quantify, and perform exploratory analysis from any text data Who this book is forNatural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. It'll help you to have prior experience of coding in Python using data types, writing functions, and importing libraries. Some experience with linguistics and probability is useful but not necessary.

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