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Python Data Analysis
Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques. Build scalable workflows for time series, NLP, image analytics, and big data processing. Key FeaturesPrepare, clean, and transform data with Python, pandas, and exploratory data analysis techniquesApply machine learning with Python using regression, classification, clustering, PCA, and Bayesian methodsScale analytics workflows using Dask, Ray, Modin, and PySparkBook DescriptionModern data analysis goes beyond cleaning and visualizing data. Today's practitioners need to build scalable data pipelines, apply machine learning, work with text and image data, and understand emerging AI techniques such as Generative AI and Large Language Models (LLMs). This guide shows you how to tackle these challenges using Python's modern data ecosystem.Unlike books focused on a single library or technique, this book provides an end-to-end approach to Python data analysis. You'll learn how to move from data preparation and exploratory analysis to machine learning, NLP, image analytics, scalable processing, and AI-powered workflows.Starting with statistical foundations, you'll learn how to clean, transform, wrangle, and visualize data. You'll then explore time series analysis, signal processing, forecasting, and predictive analytics before applying machine learning techniques such as regression, classification, clustering, PCA, probabilistic methods, and Bayesian approaches.The book also covers graph analytics, sentiment analysis, NLP, image analytics, Generative AI, and LLMs. Finally, you'll learn to scale analytics workflows using Dask, Modin, Ray, and PySpark.By the end of the book, you'll be able to build end-to-end data analysis pipelines and apply modern data science and AI techniques to solve real-world challenges.What you will learnPrepare, clean, and transform data for exploratory data analysis and data wranglingAnalyze and visualize data using Python and pandasPerform time series analysis, forecasting, and signal processingApply machine learning with Python using scikit-learn techniquesUse regression, classification, clustering, PCA, and Bayesian methodsPerform sentiment analysis, NLP, graph analytics, and image analyticsAccelerate workflows using Dask, Modin, and RayBuild scalable big data analytics pipelines with PySparkWho this book is forThis book is for data analysts, data scientists, business analysts, statisticians, students, and academic professionals who want to strengthen their Python Data Analysis skills. It is ideal for readers looking to apply data science with Python to real-world problems involving data preparation, visualization, machine learning, NLP, image analytics, and big data processing. A basic understanding of mathematics and working knowledge of Python will help you get the most from this book.
R 1,465.00
Introduction to Python Programming for Scientists and Engineers
Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
R 3,017.00
Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
R 6,893.02
Python Deep Learning
Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using PythonKey FeaturesUnderstand the theory, mathematical foundations and the structure of deep neural networksBecome familiar with transformers, large language models, and convolutional networksLearn how to apply them on various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe field of deep learning has developed rapidly in the past years and today covers broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks, they can solve, such as chat bots and text-to-image generation.By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models or adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.What you will learnEstablish theoretical foundations of deep neural networksUnderstand convolutional networks and apply them in computer vision applicationsBecome well versed with natural language processing and recurrent networksExplore the attention mechanism and transformersApply transformers and large language models for natural language and computer visionImplement coding examples with PyTorch, Keras, and Hugging Face TransformersUse MLOps to develop and deploy neural network modelsWho this book is forThis book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.
R 1,693.00
Programmieren mit Python
Diese einmalige und in sich geschlossene Einführung in die Programmierung verwendet Python um in die wichtigsten Paradigmen der Programmierung einzuführen: die Prozedurale Programmierung, dieObjektorientierte Programmierung und die Funktionale Programmierung und schließlich die Programmierung mit Numpy. Das Buch eignet sich für eine 5 – 7 ECTS Einführungsvorlesung und enthält eine Vielzahl von Aufgaben, die auch von Studierenden im Selbststudium gut zum Üben verwendet werden können.
R 1,465.00
Python Deep Learning Projects
Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient wayWhat you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects.It is assumed that you have sound knowledge of Python programming
R 1,661.00
R 488.00
Python for Signal Processing
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to “experiment and learn” as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
R 3,205.21
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 2,674.00
Practical Guide to Applied Conformal Prediction in Python
Take your machine learning skills to the next level by mastering the best framework for uncertainty quantification - Conformal PredictionKey FeaturesMaster Conformal Prediction, a fast-growing ML framework, with Python applications.Explore cutting-edge methods to measure and manage uncertainty in industry applications.The book will explain how Conformal Prediction differs from traditional machine learning.Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. "Practical Guide to Applied Conformal Prediction in Python" addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework set to revolutionize uncertainty management in various ML applications.Embark on a comprehensive journey through Conformal Prediction, exploring its fundamentals and practical applications in binary classification, regression, time series forecasting, imbalanced data, computer vision, and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. Practical examples in Python using real-world datasets reinforce intuitive explanations, ensuring you acquire a robust understanding of this modern framework for uncertainty quantification.This guide is a beacon for mastering Conformal Prediction in Python, providing a blend of theory and practical application. It serves as a comprehensive toolkit to enhance machine learning skills, catering to professionals from data scientists to ML engineers.What you will learnThe fundamental concepts and principles of conformal predictionLearn how conformal prediction differs from traditional ML methodsApply real-world examples to your own industry applicationsExplore advanced topics - imbalanced data and multi-class CPDive into the details of the conformal prediction frameworkBoost your career as a data scientist, ML engineer, or researcherLearn to apply conformal prediction to forecasting and NLPWho this book is forIdeal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.
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R 537.57
R 489.00
Natural Language Processing on Oracle Cloud Infrastructure
This book demonstrates how to use Oracle Cloud Infrastructure (OCI) and Hugging Face technologies to develop advanced NLP solutions. Through a practical case study, it addresses common NLP challenges and offers strategies for creating efficient, cost-effective transformer-based models. By the end of this book, you will have the skills and knowledge to create cutting-edge NLP solutions on OCI, customized to meet the needs of various industries and projects.The book takes you through the complete NLP solution life cycle—covering data preparation, model fine-tuning, deployment, and monitoring—while highlighting key topics such as cost-effectiveness and responsible AI for NLP implementations. Drawing from real-world experience and offering practical insights, it bridges the gap between theory and practice, equipping you to design and deploy scalable, cost-efficient NLP solutions. What You Will LearnMaster key NLP concepts and the OCI ecosystemCreate high-quality datasets using Hugging Face and OCI Data Labeling ServiceFine-tune domain-specific pre-trained models from Hugging Face using OCI Data Science Notebook SessionsDeploy and operationalize your models with OCI Data Science Model DeploymentsAutomate the NLP life cycle with OCI Data Science PipelinesImplement cost-effective strategies throughout the entire NLP life cycle, from dataset preparation to model training and deployment Who This Book Is ForA diverse audience interested in implementing NLP solutions on Oracle Cloud Infrastructure: NLP practitioners, data scientists, and machine learning engineers who want to learn how to leverage Oracle AI and Hugging Face to implement an end-to-end NLP solution life cycle, from data preparation to model deployment; Oracle practitioners who want to expand their Oracle expertise by exploring OCI's advanced capabilities for building and scaling cutting-edge NLP solutions in enterprise environments; business decision makers who want to discover the strategic benefits of NLP solutions on OCI, including cost-effectiveness and responsible AI, while driving business value
R 1,482.00
Python for Engineers and Scientists
This book equips technical professionals with a complete, structured path to mastering Python for real-world engineering and scientific applications. As Python becomes central to research labs, automation, simulation, and data-driven engineering, the learning curve remains steep. This book bridges that gap with a practical roadmap from fundamentals to professional-grade scientific computing.The book begins by building a strong programming foundation—covering Python’s architecture, essential syntax, data structures, modules, functions, control flow, and object-oriented design. Readers learn not only how to write Python, but why the language behaves the way it does, ensuring deep understanding and strong problem-solving skills. From there, the focus shifts to practical engineering tools. Dedicated chapters train readers in file handling, math modules, and key scientific libraries including NumPy, SciPy, Pandas, and Matplotlib, enabling efficient computation, signal processing, data analysis, and high-quality visualization. Advanced chapters then showcase Python’s versatility through GUI development with Kivy, database work with SQLAlchemy, and hands-on control system modeling and analysis. Each topic is rooted in industry and academic scenarios, making the content directly applicable to automation, simulation, data workflows, and engineering computation.Whether you're a student building foundational skills or a professional modernizing engineering workflows, this guide provides the programming depth, scientific tools, and applied knowledge needed to use Python confidently for prototyping, simulation, analysis, and automation. It is the one-stop resource for mastering Python in technical disciplines.What you will learn:Learn core Python programming concepts, including variables, loops, functions, and object-oriented principles in a scientific and engineering context.Build strong skills in key scientific Python libraries such as NumPy, Pandas, Matplotlib, and SciPy for computation, analysis, and visualization.Apply Python to real-world engineering scenarios, including dynamic control systems and automation workflows.Develop interactive engineering applications by creating graphical user interfaces (GUIs) with Kivy. Who this book is for:This book is intended for practicing engineers and scientists across various disciplines of engineering who need to use Python for complex problem-solving, simulation, and data analysis in their work.
R 1,368.00
R 1,995.00
R 333.00
Mastering Machine Learning with Python in Six Steps
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
R 1,482.00
R 1,627.73
R 2,203.58
R 1,119.17
Python
This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1959.
R 3,090.00
R 1,517.00
R 815.07
R 815.33
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
Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
R 8,572.00
R 336.00
Python Scripting for Computational Science
With a primary focus on examples and applications of relevance to computational scientists, this brilliantly useful book shows computational scientists how to develop tailored, flexible, and human-efficient working environments built from small scripts written in the easy-to-learn, high-level Python language. All the tools and examples in this book are open source codes. This third edition features lots of new material. It is also released after a comprehensive reorganization of the text. The author has inserted improved examples and tools and updated information, as well as correcting any errors that crept in to the first imprint.
R 2,172.44
Introduction to Python
Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.FeaturesCovers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programmingSuitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionalsSolutions manual, code, and additional examples are available for download
R 3,260.00
Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
R 3,087.35
R 901.00
Knowing NLP
Knowing NLP is a book designed for beginners yet also Practitioners and Trainers of NLP who want to really understand what hides behind the techniques. Terry Elston lifts up the lid on Neuro Linguistic Programming and reveals interesting insights to what is commonly accepted as 'normal' practise. "Knowing NLP" assists the seeker who truly wants to learn his/her trade in NLP and gain wisdom from understanding the principles behind it. All the popular techniques are listed and discussed, including Conversational Anchoring & Time Based Techniques - the successor of Time Line Therapy. You will also discover that gaining Rapport in NLP terms is not about Matching and Mirroring as is generally taught. So if you really want to find out what an earth NLP is all about, take a peek inside these covers. You will be glad you came on this journey with Terry Elston.
R 519.00
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
Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
R 1,311.25
R 703.81
R 1,197.94
Python for Signal Processing
This book covers the fundamental concepts in signal processing illustrated with Python code and made available via IPython Notebooks, which are live, interactive, browser-based documents that allow one to change parameters, redraw plots, and tinker with the ideas presented in the text. Everything in the text is computable in this format and thereby invites readers to “experiment and learn” as they read. The book focuses on the core, fundamental principles of signal processing. The code corresponding to this book uses the core functionality of the scientific Python toolchain that should remain unchanged into the foreseeable future. For those looking to migrate their signal processing codes to Python, this book illustrates the key signal and plotting modules that can ease this transition. For those already comfortable with the scientific Python toolchain, this book illustrates the fundamental concepts in signal processing and provides a gateway to further signal processing concepts.
R 4,120.61
R 817.57
R 519.00
R 1,087.16
Natural Language Processing - NLP 2000
This volume contains the papers prepared for the 2nd International Conference on Natural Language Processing, held 2-4 June in Patras, Greece. The conference program features invited talks and submitted papers, c- ering a wide range of NLP areas: text segmentation, morphological analysis, lexical knowledge acquisition and representation, grammar formalism and s- tacticparsing,discourse analysis,languagegeneration,man-machineinteraction, machine translation, word sense disambiguation, and information extraction. The program committee received 71 abstracts, of which unfortunately no more than 50% could be accepted. Every paper was reviewed by at least two reviewers. The fairness of the reviewing process is demonstrated by the broad spread of institutions and countries represented in the accepted papers. So many have contributed to the success of the conference. The primary credit, ofcourse, goes to theauthors andto the invitedspeakers. By theirpapers and their inspired talks they established the quality of the conference. Secondly, thanks should go to the referees and to the program committee members who did a thorough and conscientious job. It was not easy to select the papers to be presented. Last, but not least, my special thanks to the organizing committee for making this conference happen.
R 1,581.34
