Natural language and machine translation

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book.

Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scaleKey FeaturesLeverage supervised learning to build binary classification, multi-class classification, and regression modelsLearn to use unsupervised learning using the K-means clustering methodMaster the art of time series forecasting using Redshift MLPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAmazon Redshift Serverless enables organizations to run petabyte-scale cloud data warehouses quickly and in a cost-effective way, enabling data science professionals to efficiently deploy cloud data warehouses and leverage easy-to-use tools to train models and run predictions.

Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters.

Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLPKey FeaturesExplore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problemsSolve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AIMonitor your model's performance with the help of BertViz, exBERT, and TensorBoardBook DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm.

One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasksKey FeaturesApply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2Explore applications like text generation, summarization, weakly supervised labelling and moreRead cutting edge material with seminal papers provided in the GitHub repository with full working codeBook DescriptionRecently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications.

A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).

Der Praxisleitfaden für die Zukunft der Softwareentwicklung mit KI-Assistenz- Erfahren Sie, wie Sie KI-Tools Schritt für Schritt in Ihre täglichen Arbeitsabläufe integrieren- Anhand konkreter Beispiele zeigt Ihnen Nathan B.

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models.

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 datasetsKey FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook DescriptionC++ can make your machine learning models run faster and more efficiently.

Learn advanced techniques to improve the performance and quality of your predictive modelsKey FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook DescriptionPython is a programming language that provides a wide range of features that can be used in the field of data science.

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learnKey FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machine learning algorithms using a range of libraries from the Python ecosystemAccomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologiesBook DescriptionMachine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system.

Learn to build end-to-end AI apps from scratch for Android and iOS using TensorFlow Lite, CoreML, and PyTorchKey FeaturesBuild practical, real-world AI projects on Android and iOSImplement tasks such as recognizing handwritten digits, sentiment analysis, and moreExplore the core functions of machine learning, deep learning, and mobile visionBook DescriptionWe're witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning.

Incorporate intelligence to your data-driven business insights and high accuracy business solutionsKey FeaturesExplore IBM Watson capabilities such as Natural Language Processing (NLP) and machine learningBuild projects to adopt IBM Watson across retail, banking, and healthcareLearn forecasting, anomaly detection, and pattern recognition with ML techniquesBook DescriptionIBM Watson provides fast, intelligent insight in ways that the human brain simply can't match.

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and KerasKey FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook DescriptionDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.

Perform efficient fast text representation and classification with Facebook's fastText libraryKey FeaturesIntroduction to Facebook's fastText library for NLPPerform efficient word representations, sentence classification, vector representationBuild better, more scalable solutions for text representation and classificationBook DescriptionFacebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP).

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook DescriptionEnsemble modeling is an approach used to improve the performance of machine learning models.

Implement TensorFlow's offerings such as TensorBoard, TensorFlow.

Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologiesKey FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook DescriptionImplementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage.

Explore various approaches to organize and extract useful text from unstructured data using JavaKey FeaturesUse deep learning and NLP techniques in Java to discover hidden insights in textWork with popular Java libraries such as CoreNLP, OpenNLP, and MalletExplore machine translation, identifying parts of speech, and topic modelingBook DescriptionNatural Language Processing (NLP) allows you to take any sentence and identify patterns, special names, company names, and more.

Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with easeKey FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook DescriptionVirtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI.

Concepts, tools, and techniques to explore deep learning architectures and methodologiesKey FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data.

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorchKey FeaturesInternals and principles of PyTorchImplement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch.

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with PythonKey FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook DescriptionRemodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI).

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken.

Design and develop high-performance programs in Julia 1.

No detailed description available for "e;UX for Enterprise ChatGPT Solutions"e;.

Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with easeKey FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook DescriptionVirtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI.

In the global research community, English has become the main language of scholarly publishing in many disciplines.

In the global research community, English has become the main language of scholarly publishing in many disciplines.

Captioning and Subtitling for d/Deaf and Hard of Hearing Audiences is a comprehensive guide to the theory and practice of captioning and subtitling, a discipline that has evolved quickly in recent years.

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 datasetsKey FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook DescriptionC++ can make your machine learning models run faster and more efficiently.

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learningKey FeaturesA no-math, code-driven programmer's guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook DescriptionNLP in Python is among the most sought after skills among data scientists.

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book.

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and KerasKey FeaturesGet to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and KerasImplement advanced concepts and popular machine learning algorithms in real-world projectsBuild analytics, computer vision, and neural network projects Book DescriptionMachine learning is transforming the way we understand and interact with the world around us.

This book gives a comprehensive introduction to natural language processing (NLP) and its applications, covering the topics of multimodal data processing, Chinese word segmentation, new word discovery, named entity recognition, keyword analysis, and knowledge graph construction in terms of semantic analysis.

Membrane computing is a class of distributed and parallel computing models inspired by living cells.

The journey towards the autonomous enterprise has begun; there are already companies operating in a highly automated way.

The journey towards the autonomous enterprise has begun; there are already companies operating in a highly automated way.

Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process.

Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field.

This book deals with a hard problem that is inherent to human language: ambiguity.

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms.