Search results for "Python и NLP"
Items we need to order for you
R 1,139.00
R 1,630.69
R 834.00
R 611.00
Neuro-linguistic Programming For Dummies
Change your behavior with neuro-linguistic programming Neuro-linguistic programming (NLP) is a psychological, practical, results-focused approach to understanding the relationship between thoughts, feelings, and behavior, and how they shape the everyday reality that people experience. Neuro-linguistic Programming For Dummies walks you through the fundamentals of NLP, helping you gain insights into how and why you—and others—think, communicate (both verbally and non-verbally), and behave; often the result of early experiences, emotional conditioning, and unconscious beliefs formed over time. Understanding the patterns that you operate daily enables you to consciously do more of what works for you to create the results you want in your life and less of what gets in the way of your success. With this deeper understanding about yourself, your goals become more tailored to who you truly are—and more achievable as a result. By understanding how others operate, you can adjust the way you communicate to create deeper rapport and exercise greater trust, influence, and clarity. Inside: Effective, straightforward strategies to increase self-awareness, build confidence, and become a clearer communicatorTechniques that, when combined with other forms of health- and self-care, can help you find relief from phobias, anxiety, and depressionClear descriptions of the central models and assumptions that underpin NLP Perfect for therapists, business coaches, counselors, and other helping professionals, Neuro-linguistic Programming For Dummies is also a must-read for busy executives, managers, entrepreneurs, and employees doing their best to navigate contemporary life.
R 680.00
R 916.00
R 519.00
R 817.57
Deep Learning with Applications Using Python
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.Use face recognition and face detection capabilitiesCreate speech-to-text and text-to-speech functionalityEngage with chatbots using deep learningWho This Book Is ForData scientists and developers who want to adapt and build deep learning applications.
R 1,949.89
When NLP meets LLM
This book looks at conversational search in intelligent dialogue systems, as it investigates and addresses the challenges pertinent to effective context incorporation in conversational question answering (ConvQA). The authors explore the possibility of designing a scalable Conversational Question Answering Agent that can handle the challenges of incomplete/ambiguous questions, better able to relate to co-references to cope with the problems of effective weights and optimal threshold selection in vehicular networks. A fundamental emphasis is the understanding of ambiguous follow-up questions and the generation of contextual and question entities to fill in the missing information gaps. Key topics are studied, such as ‘hard history selection’ to filter out the context that is not relevant and performing a re-ranking of the selected turns based on their significance to answer the question as a part of the soft history selection process.This book aims to demonstrate that the history selection and modelling approaches proposed can effectively improve the performance of ConvQA models in different settings. The proposed models are compared with the state-of-the-art vis-à-vis different conversational datasets and provide new insights into conversational information retrieval. Through a systematic study of structured representations, entity-aware history selection, and open-domain passage retrieval using contrastive learning, this book presents a robust framework for advancing multi-turn QA systems.It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.
R 3,612.06
AI Projects in PyTorch
Dive into computer vision, natural language processing, and recommender systems by building end-to-end projects in PyTorch — one of the most widely used deep learning frameworks among researchers and engineers worldwide. This book takes you from the fundamentals to complete, hands-on projects, giving you the confidence to start creating your own AI solutions.The book begins with a chapter on the fundamentals of machine learning, laying the groundwork by introducing key aspects of an ML project such as data preprocessing, feature engineering, model training, and evaluation, along with essential concepts like overfitting and underfitting. The following chapter, "Tensors in PyTorch," explores data handling in PyTorch -- from basic tensor operations to advanced gradient computations -- providing a deeper understanding of data transformations.With the foundations in place, the book moves on to hands-on projects. Chapter 3 introduces you to the world of computer vision, where you will build an image classifier using convolutional neural networks. The next three chapters immerse you in natural language processing: beginning with text classification (Chapter 4), tackling a range of NLP tasks with Hugging Face (Chapter 5), and culminating in the creation of a storytelling language model (Chapter 6).The focus then shifts to other key AI domains – you will tackle an audio classification task (Chapter 7), build a recommender system in PyTorch (Chapter 8), and finish with a multi-modal project that combines computer vision and natural language processing to build an image captioning system (Chapter 9).Whether you're a software engineer looking to break into the world of AI or a beginner with basic Python skills, "AI Projects with PyTorch" offers practical guidance and hands-on experience to start building your own AI applications with confidence.What you will learn:Master the core principles of machine learning and gain confidence with the typical PyTorch project workflow.Build a solid understanding of data handling in PyTorch – including tensors, datasets, data loaders, and gradient computations.Build natural language processing models, from text classification to storytelling language models.Work on multiple natural language processing tasks with Hugging Face libraries.Combine vision and language to build an image captioning system. Who this is book is for:- Python programmers and software engineers who are new to AI and want a practical, project-based introduction with PyTorch. ML engineers who wish to expand into other AI domains such as computer vision, natural language processing, audio processing, etc.
R 1,254.00
R 458.00
Data-Driven SEO with Python
Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
R 912.00
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
R 1,482.00
Hands-On Transfer Learning with Python
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. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learnSet up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL modelsExplore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functionsGet to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLPWho this book is forHands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
R 1,661.00
R 2,543.00
R 1,221.00
Generative AI Foundations in Python
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application.Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs.By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learnDiscover the fundamentals of GenAI and its foundations in NLPDissect foundational generative architectures including GANs, transformers, and diffusion modelsFind out how to fine-tune LLMs for specific NLP tasksUnderstand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as financeExplore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAGImplement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputsWho this book is forThis book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
R 1,368.00
Advanced Deep Learning with Python
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
R 1,661.00
NLP
NLP (Neuro-Linguistic Programming) has already helped millions of people overcome fears, increase confidence, enrich relationships, and achieve greater success. Now, from the company and training team behind NLP: The New Technology of Achievement, one of the bestselling NLP books of all time, comes "NLP: A Power User's Manual". Written by an NLP Master Practitioner and Training Coach, with an introduction from the President of NLP Comprehensive, "NLP: The Power User's Manual guides" users to peak performance in business and life, and gets specific results. In twelve illuminating sections, "NLP: A Power User's Manual" leads you through dozens of discoveries-revelations of NLP practice that enable you to explore your own personal thinking patterns, to manage them-and to transform them. Divided into two categories, "All About You" and "All About the Other Guy", these strategies offer a personal and interpersonal program that frees you to become better at managing your feelings instead of being dominated by them, managing your motivations, being less judgemental, more productive, more confident, more flexible, more persuasive, liked, and respected.Chapters on Personal Remodeling (Discovery 9: No inner enemy) and Secrets of Making Your Point (Discovery 31: Convey understanding and safety without talking), enhance creativity, collaboration, cooperation, and communication. Through mind reading techniques-non-verbal communication, and hearing what's missing-learn the secrets of relating with others, understanding how they are thinking-and influencing them. A streamlined all-purpose guide for both newcomers and NLP veterans, "NLP: A Power User's Manual" is the new all-in-one, eye-opening blueprint for your own ultimate success.
R 456.00
R 1,221.00
R 815.33
R 1,008.00
R 611.00
R 1,220.00
R 1,482.00
R 489.00
R 351.00
R 815.07
R 237.00
R 814.30
R 1,367.00
Natural Language Processing in Artificial Intelligence—NLPinAI 2020
This book covers theoretical work, applications, approaches, and techniques for computational models of information and its presentation by language (artificial, human, or natural in other ways). Computational and technological developments that incorporate natural language are proliferating. Adequate coverage encounters difficult problems related to ambiguities and dependency on context and agents (humans or computational systems). The goal is to promote computational systems of intelligent natural language processing and related models of computation, language, thought, mental states, reasoning, and other cognitive processes.
R 5,997.01
NLP Application
Applying Natural Language Processing (NLP) concepts to help humans in their daily life, this book discusses an automatic translation of an unstructured Natural Language Question (NLQ) into a Structured Query Language (SQL) statement. Using SQL as a Relational DataBase (RDB) interaction language, database administrators or general users with little to no SQL querying abilities are provided with all the knowledge necessary to perform queries on RDBs in an interactive manner. Key Features:Includes extensive and illustrative examples to simplify the discussed conceptsDiscusses a novel, and yet simple, approach to NLPIntroduces a lightweight NLQ into SQL translation approach through the use of RDB MetaTables as a Hash tableExtensive literature review and thorough background information on every tool, concept and technique appliedProviding a unique approach to NLQ into SQL translation, as well as comprising disparate resources on NLP as a whole, this shortform book is of direct use to administrators and general users of databases.
R 3,610.15
Ontologies with Python
Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python.What You Will LearnUse Owlready2 to access and modify OWL ontologies in PythonPublish ontologies on dynamic websitesPerform automatic reasoning in PythonUse well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System)Integrate Python methods in OWL ontologies Who Is This Book ForBeginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.
R 1,254.00
Python Using GPT-5 and Gemini
Python Using GPT-5 and Gemini provides a practical, up-to-date guide that combines solid Python programming skills with real-world integration of today’s leading large language models. The book teaches essential Python techniques while showing readers exactly how to connect and use GPT-5, Google Gemini, Claude, Grok, Perplexity, and other major models in their daily work.Readers will gain a strong foundation in Python data structures, comprehensions, generators, regular expressions, and object-oriented programming. The book then moves into detailed coverage of GPT-5 (including GPT-5.4 and GPT-5.3 Instant), Gemini 3.1, Claude Mythos and Opus 4.6, along with practical Python code samples for chatbots, tool calling, data analysis, graphics generation, and integration with PowerPoint and Excel. Additional chapters explore LLM benchmarks, deceptive behaviors such as alignment faking and scheming, military AI applications, and comparisons between the major models, giving readers clear insight into their relative strengths and limitations.This book is written for intermediate Python developers, data scientists, technical professionals, and anyone who wants to move beyond basic prompting and build production-ready applications that effectively combine Python with the latest generation of large language models.
R 6,118.00
