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R 397.00
Quick Start Guide to Large Language Models
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like ChatGPT are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, hands-on exercises, and more. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, parameters, and performance. You'll find even more resources on the companion website, including sample datasets and code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and ChatGPT), Google (BERT, T5, and Bard), EleutherAI (GPT-J and GPT-Neo), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for retrieval-augmented generationMaster advanced prompt engineering techniques like output structuring, chain-ofthought, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user dataConstruct and fine-tune multimodal Transformer architectures using opensource LLMsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF)Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind "By balancing the potential of both open- and closed-source models, Quick Start Guide to Large Language Models stands as a comprehensive guide to understanding and using LLMs, bridging the gap between theoretical concepts and practical application."--Giada Pistilli, Principal Ethicist at HuggingFace "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,628.00
Quick Start Guide to Large Language Models
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI AgentsMaster advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAIConstruct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasetsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mindDiagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
R 1,199.00
Essential Guide to LLMOps
Unlock the secrets to mastering LLMOps with innovative approaches to streamline AI workflows, improve model efficiency, and ensure robust scalability, revolutionizing your language model operations from start to finishKey FeaturesGain a comprehensive understanding of LLMOps, from data handling to model governanceLeverage tools for efficient LLM lifecycle management, from development to maintenanceDiscover real-world examples of industry cutting-edge trends in generative AI operationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe rapid advancements in large language models (LLMs) bring significant challenges in deployment, maintenance, and scalability. This Essential Guide to LLMOps provides practical solutions and strategies to overcome these challenges, ensuring seamless integration and the optimization of LLMs in real-world applications.This book takes you through the historical background, core concepts, and essential tools for data analysis, model development, deployment, maintenance, and governance. You’ll learn how to streamline workflows, enhance efficiency in LLMOps processes, employ LLMOps tools for precise model fine-tuning, and address the critical aspects of model review and governance. You’ll also get to grips with the practices and performance considerations that are necessary for the responsible development and deployment of LLMs. The book equips you with insights into model inference, scalability, and continuous improvement, and shows you how to implement these in real-world applications.By the end of this book, you’ll have learned the nuances of LLMOps, including effective deployment strategies, scalability solutions, and continuous improvement techniques, equipping you to stay ahead in the dynamic world of AI.What you will learnUnderstand the evolution and impact of LLMs in AIDifferentiate between LLMOps and traditional MLOpsUtilize LLMOps tools for data analysis, preparation, and fine-tuningMaster strategies for model development, deployment, and improvementImplement techniques for model inference, serving, and scalabilityIntegrate human-in-the-loop strategies for refining LLM outputsGrasp the forefront of emerging technologies and practices in LLMOpsWho this book is forThis book is for machine learning professionals, data scientists, ML engineers, and AI leaders interested in LLMOps. It is particularly valuable for those developing, deploying, and managing LLMs, as well as academics and students looking to deepen their understanding of the latest AI and machine learning trends. Professionals in tech companies and research institutions, as well as anyone with foundational knowledge of machine learning will find this resource invaluable for advancing their skills in LLMOps.
R 1,531.00
R 736.72
Machine Learning with Go Quick Start Guide
This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and ClusteringKey FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook DescriptionMachine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.What you will learnUnderstand the types of problem that machine learning solves, and the various approachesImport, pre-process, and explore data with Go to make it ready for machine learning algorithmsVisualize data with gonum/plot and GophernotesDiagnose common machine learning problems, such as overfitting and underfittingImplement supervised and unsupervised learning algorithms using Go librariesBuild a simple web service around a model and use it to make predictionsWho this book is forThis book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.
R 912.00
R 214.00
Machine Learning with Scala Quick Start Guide
Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.Key FeaturesConstruct and deploy machine learning systems that learn from your data and give accurate predictionsUnleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j libraryBook DescriptionScala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.What you will learnGet acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4jLearn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured dataUnderstand supervised and unsupervised learning techniques with best practices and pitfallsLearn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques Learn effective ways of clustering analysis with dimensionality reduction techniquesLearn recommender systems with collaborative filtering approachDelve into deep learning and neural network architecturesWho this book is forThis book is for machine learning developers looking to train machine learning models in Scala without spending too much time and effort. Some fundamental knowledge of Scala programming and some basics of statistics and linear algebra is all you need to get started with this book.
R 1,140.00
Machine Learning with R Quick Start Guide
Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.Key FeaturesUse R 3.5 to implement real-world examples in machine learningImplement key machine learning algorithms to understand the working mechanism of smart modelsCreate end-to-end machine learning pipelines using modern libraries from the R ecosystemBook DescriptionMachine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.What you will learnIntroduce yourself to the basics of machine learning with R 3.5Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your resultsLearn to build predictive models with the help of various machine learning techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresUse interactive data analysis with R to get insights into dataImplement supervised and unsupervised learning, and NLP using R librariesWho this book is forThis book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
R 1,140.00
Deep Learning with PyTorch Quick Start Guide
Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.Key FeaturesClear and concise explanationsGives important insights into deep learning modelsPractical demonstration of key conceptsBook DescriptionPyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.What you will learnSet up the deep learning environment using the PyTorch libraryLearn to build a deep learning model for image classificationUse a convolutional neural network for transfer learningUnderstand to use PyTorch for natural language processingUse a recurrent neural network to classify textUnderstand how to optimize PyTorch in multiprocessor and distributed environmentsTrain, optimize, and deploy your neural networks for maximum accuracy and performanceLearn to deploy production-ready modelsWho this book is forDevelopers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.
R 1,140.00
ReasonML Quick Start Guide
A hands on approach to learning ReasonML from the perspective of a web developer.Key FeaturesHands on learning by building a real world app shell that includes client-side routing and more.Understand Reason’s ecosystem including BuckleScript and various npm workflows.Learn how Reason differs from TypeScript and Flow, and how to use it to make refactoring less stressful.Book DescriptionReasonML, also known as Reason, is a new syntax and toolchain for OCaml that was created by Facebook and is meant to be approachable for web developers. Although OCaml has several resources, most of them are from the perspective of systems development. This book, alternatively, explores Reason from the perspective of web development.You'll learn how to use Reason to build safer, simpler React applications and why you would want to do so. Reason supports immutability by default, which works quite well in the context of React.In learning Reason, you will also learn about its ecosystem – BuckleScript, JavaScript interoperability, and various npm workflows. We learn by building a real-world app shell, including a client-side router with page transitions, that we can customize for any Reason project. You'll learn how to leverage OCaml's excellent type system to enforce guarantees about business logic, as well as preventing runtime type errors.You'll also see how the type system can help offload concerns that we once had to keep in our heads.We'll explore using CSS-in-Reason, how to use external JSON in Reason, and how to unit-test critical business logic. By the end of the book, you'll understand why Reason is exploding in popularity and will have a solid foundation on which to continue your journey with Reason.What you will learnLearn why Reason is exploding in popularity and why it's the future of ReactBecome familiar with Reason's syntax and semanticsLearn about Reason's ecosystem: BuckleScript and JavaScript interoperabilityLearn how to build React applications with ReasonLearn how to use Reason's type system as a tool to provide amazing guaranteesGain a solid foundation on which to continue your journeyWho this book is forThe target audience of this book is web developers who are somewhat familiar with ReactJS and who want to learn why ReasonML is the future of ReactJS.
R 1,140.00
Machine Learning with Apache Spark Quick Start Guide
Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-timeKey FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook DescriptionEvery person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is forThis book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.
R 1,140.00
Natural Language Processing with Python Quick Start Guide
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. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
R 1,140.00
fastText Quick Start Guide
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). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects.What you will learnCreate models using the default command line options in fastTextUnderstand the algorithms used in fastText to create word vectorsCombine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipelineExplore word representation and sentence classification using fastTextUse Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficientlyDevelop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorchWho this book is forThis book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.
R 1,140.00
TensorFlow 2.0 Quick Start Guide
Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.Key FeaturesTrain your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networksGet acquainted with some new practices introduced in TensorFlow 2.0 AlphaBook DescriptionTensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.What you will learnUse tf.Keras for fast prototyping, building, and training deep learning neural network modelsEasily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible filesUse TensorFlow to tackle traditional supervised and unsupervised machine learning applicationsUnderstand image recognition techniques using TensorFlowPerform neural style transfer for image hybridization using a neural networkCode a recurrent neural network in TensorFlow to perform text-style generationWho this book is forData scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.
R 1,140.00
Lua Quick Start Guide
The easiest way to learn Lua programmingKey FeaturesThe easiest way to learn Lua codingUse the Lua standard libraries and debug Lua codeEmbed Lua as a scripting language using the Lua C APIBook DescriptionLua is a small, powerful and extendable scripting/programming language that can be used for learning to program, and writing games and applications, or as an embedded scripting language. There are many popular commercial projects that allow you to modify or extend them through Lua scripting, and this book will get you ready for that. This book is the easiest way to learn Lua. It introduces you to the basics of Lua and helps you to understand the problems it solves. You will work with the basic language features, the libraries Lua provides, and powerful topics such as object-oriented programming. Every aspect of programming in Lua, variables, data types, functions, tables, arrays and objects, is covered in sufficient detail for you to get started. You will also find out about Lua's module system and how to interface with the operating system.After reading this book, you will be ready to use Lua as a programming language to write code that can interface with the operating system, automate tasks, make playable games, and much more. This book is a solid starting point for those who want to learn Lua in order to move onto other technologies such as Love2D or Roblox.A quick start guide is a focused, shorter title that provides a faster paced introduction to a technology. It is designed for people who don't need all the details at this point in their learning curve. This presentation has been streamlined to concentrate on the things you really need to know.What you will learnUnderstand the basics of programming the Lua languageUnderstand how to use tables, the data structure that makes Lua so powerfulUnderstand object-oriented programming in Lua using metatablesUnderstand standard LUA libraries for math, file io, and moreManipulate string data using LuaUnderstand how to debug Lua applications quickly and effcientlyUnderstand how to embed Lua into applications with the Lua C APIWho this book is forThis book is for developers who want to get up and running with Lua. This book is ideal for programmers who want to learn to embed Lua in their own applications, as well as for beginner programmers who have never coded before.
R 1,140.00
R 405.00
R 733.00
Quick Home School Starter Guide
The Quick Home School Starter Guide lists questions and answers for families that are making or considering a transition to home education themselves. It includes information on: Law Philosophies Curricula Testing and Evaluation Disabilities and Health Support Materials Post-secondary Socialization Support Organizations Reasons to Homeschool Perks and Costs The Tough Questions Dad's Point of View Resource Links
R 442.58
Quick Start Guide to FFmpeg
Create, edit, modify and convert multimedia files using FFmpeg, the most versatile open source audio and video processing tool available for Linux, Mac and Windows users. After reading this book, you will be able to work with video and audio files, images, text, animations, fonts, subtitles and metadata like a pro.It begins with a simple introduction to FFmpeg executables — ffmpeg, ffprobe and ffplay, and explains how you can use them to process multimedia containers, streams, audio channels, maps and metadata. It then describes how you can easily edit, enhance and convert audio, video, image and text files. There are dedicated chapters for filters, audio, subtitles and metadata, as well as FFmpeg tips and tricks. Sample lists of FFmpeg filters, encoders, decoders, formats and codecs are also available as appendices.Quick Start Guide to FFmpeg is for anyone who needs to edit or process multimedia files including studio professionals, broadcast personnel, content creators, podcasters, librarians, archivists and webmasters. It will be indispensable for those wanting to process a variety of multimedia files from the command line and inside shell scripts or custom-built software.You Will Learn To:Convert from one format to another e.g. video-to-video, video-to-audio, video-to-image, image-to-video, video-to-animation, animation-to-video, text-to-audio, text-to-videoEdit video files by cutting them with and without re-encoding, appending, resizing, changing frame rate and aspect ratio, mixing in audioUse filters to rotate, flip, crop, overlay (side-by-side or inset), remove logos, blur, smooth and sharpen, apply transitions as well as speed up or down playbackEdit audio files by changing, normalizing or compressing volume, mixing and splitting channels and detecting silence. Also, learn to generate waveforms as video or imagesAdd subtitles, place them anywhere on the screen, use custom fonts and colors, and use different languagesLearn how to import, export and remove metadata, add MP3 tags (including album art), set global and stream-specific metadata, export and remove metadataThis Book For:Content creators and bloggers from professional studio employees to Youtubers and hobbyists who need to process their own multimedia content; multimedia archivists and librarians; regular Linux desktop users
R 1,254.00
QGIS Quick Start Guide
Step through loading GIS data, creating GIS data, styling GIS and making maps with QGIS following a simple narrative that will allow you to build confidence as you progress.Key FeaturesWork with GIS data, a step by step guide from creation to making a mapPerform geoprocessing tasks and automate them using model builderExplore a range of features in QGIS 3.4, discover the power behind open source desktop GISBook DescriptionQGIS is a user friendly, open source geographic information system (GIS). The popularity of open source GIS and QGIS, in particular, has been growing rapidly over the last few years. This book is designed to help beginners learn about all the tools required to use QGIS 3.4.This book will provide you with clear, step-by-step instructions to help you apply your GIS knowledge to QGIS. You begin with an overview of QGIS 3.4 and its installation. You will learn how to load existing spatial data and create vector data from scratch. You will then be creating styles and labels for maps. The final two chapters demonstrate the Processing toolbox and include a brief investigation on how to extend QGIS. Throughout this book, we will be using the GeoPackage format, and we will also discuss how QGIS can support many different types of data.Finally, you will learn where to get help and how to become engaged with the GIS community.What you will learnUse existing data to interact with the canvas via zoom/pan/selectionCreate vector data and a GeoPackage and build a simple project around itStyle data, both vector and raster data, using the Layer Styling PanelDesign, label, save, and export maps using the data you have createdAnalyze spatial queries using the Processing toolboxExpand QGIS with the help of plugins, model builder, and the command lineWho this book is forIf you know the basic functions and processes of GIS, and want to learn to use QGIS to analyze geospatial data and create rich mapping applications, then this is the book for you.
R 1,140.00
Machine Learning with scikit-learn Quick Start Guide
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering.Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is forThis book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
R 1,140.00
Parent’s Quick Start Guide to Dyslexia
Parent’s Quick Start Guide to Dyslexia provides parents and caregivers with an immediate overview of dyslexia and steps they can take to support and encourage their child.Each chapter is packed with detailed and helpful information, covering identification, public schools versus private settings, and how (and when) to seek professional help. Summary and resource sections at the end of each chapter give quick guidance to busy readers. Topics include a wealth of research-backed activities, nurturing talent and creativity, motivating your child to read, and more.Offering straightforward, easy to understand, and evidence-based information, this book is a go-to resource for caregivers parenting a child with dyslexia.
R 651.00
Kotlin Quick Start Guide
Get started with Kotlin programming for building real world applicationsKey FeaturesStart programming with KotlinExplore Kotlin language syntax, standard libraries and Java InteroperabilityBuilds an example application with what you learnBook DescriptionKotlin is a general purpose, object-oriented language that primarily targets the JVM and Android. Intended as a better alternative to Java, its main goals are high interoperability with Java and increased developer productivity. Kotlin is still a new language and this book will help you to learn the core Kotlin features and get you ready for developing applications with Kotlin.This book covers Kotlin features in detail and explains them with practical code examples.You will learn how to set up the environment and take your frst steps with Kotlin and its syntax. We will cover the basics of the language, including functions, variables, and basic data types. With the basics covered, the next chapters show how functions are first-class citizens in Kotlin and deal with the object-oriented side of Kotlin.You will move on to more advanced features of Kotlin. You will explore Kotlin's Standard Library and learn how to work with the Collections API. The book finishes by putting Kotlin in to practice, showing how to build a desktop app.By the end of this book, you will be confident enough to use Kotlin for your next project.What you will learnProgramming in Kotlin language syntax, basic types, control ?ow, classes, and OOPWriting functions and functional programming in KotlinDefning and importing from packages in KotlinRunning Kotlin on JVMs and Android runtimesWorking with the Kotlin Standard Library and advanced features of Kotlin programmingSetting up a Kotlin development environment with JetBrains toolsBuilding real-world applications with KotlinWho this book is forThis book is intended for anybody who wants to learn the most important Kotlin features. No experience of Kotlin is expected.
R 1,140.00
Ansible Quick Start Guide
Configure Ansible and start coding YAML playbooks using the appropriate modulesKey FeaturesCreate and use Ansible Playbook to script and organise management tasksBenefit from the Ansible community roles and modules to resolve complex and niche tasksWrite configuration management code to automate infrastructureBook DescriptionConfiguration Management (CM) tools help administrators reduce their workload. Ansible is one of the best Configuration Management tools, and can act as an orchestrator for managing other CMs. This book is the easiest way to learn how to use Ansible as an orchestrator and a Configuration Management tool. With this book, you will learn how to control and monitor computer and network infrastructures of any size,physical or virtual. You will begin by learning about the Ansible client-server architecture. To get started, you will set up and configure an Ansible server. You will then go through the major features of Ansible: Playbook and Inventory. Then, we will look at Ansible systems and network modules. You will then use Ansible to enable infrastructure automated configuration management, followed by best practices for using Ansible roles and community modules. Finally, you will explore Ansible features such as Ansible Vault, Ansible Containers, and Ansible plugins.What you will learnImplement Playbook YAML scripts and its capacities to simplify day-to-day tasksSetup Static and Dynamic InventoryUse Ansible predefined modules for Linux, Windows, networking, and virtualisation administrationOrganize and configure the host filesystem using storage and files modulesImplement Ansible to enable infrastructure automated configuration managementSimplify infrastructure administrationSearch and install new roles and enable them within AnsibleSecure your data using Ansible VaultWho this book is forThis book is targeted at System Administrators and Network Administrators who want to use Ansible to automate an infrastructure. No knowledge of Ansible is required.
R 1,140.00
Quick Guide to Meeting the Teachers' Standards Part 1
This is a quick and handy guide to evidencing and meeting Part 1 of the Teachers’ Standards for trainee teachers. It looks at every strand of Part 1 of the Standards, encourages critical reflection throughout and puts pupil learning and progress at the heart of things while detailing how you can best evidence this. It includes:a general introduction to the Standards and how to meet them;a clear explanation of each strand of the Standards in easy to follow English;guidance on the best evidence to select and how to present it;advice on some common pitfalls;space to record your evidence as you go along;a ten-point checklist summarising all the key points in the guide.All trainee teachers have to meet the Teachers’ Standards in order to gain qualified teacher status, yet many are unsure what this entails. A quick guide to Meeting the Teachers’ Standards: Part 1 is very helpful in this respect. Using key words from the Standards, this book helps trainees to understand exactly what they need to do and the evidence that will demonstrate this. The guide is clear, concise and contains useful pointers on things to do and to avoid. It is a most useful resource for anyone embarking on teacher training.Neil Brading, Outset Teachers' Standards Ltd
R 1,372.17
R 411.35
Quick–Start Guide to the Whole Bible, The Understa nding the Big Picture Book–by–Book
A Friendly Overview of Every Book of the BibleReading the Bible can be intimidating. Made up of sixty-six "books" ranging in subject matter from history to instruction, from poetry to prophecy, this all-time bestseller is frequently misunderstood, even by those who read it regularly.In this easy-to-read handbook, two respected Bible professors will help you make sense of the Bible, book-by-book. Using a conversational tone, they cut right to the heart of the matter, focusing on two main questions: "What does it say?" and "Why does it matter?"Whether you're a seasoned reader of Scripture or reading it for the first time, you're sure to come away with a better understanding of what the Bible is all about and why it is the most important book you'll ever read.
R 521.00
Recurrent Neural Networks with Python Quick Start Guide
Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook DescriptionDevelopers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood.After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field.What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is forThis book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
R 1,140.00
TensorFlow Reinforcement Learning Quick Start Guide
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasksKey FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book DescriptionAdvances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving.The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator.By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems.What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is forData scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
R 912.00
Microsoft Power BI Quick Start Guide
Bring your data to life with Power BIKey FeaturesGet to grips with the fundamentals of Microsoft Power BI and its Business Intelligence capabilitiesBuild accurate analytical models, reports and dashboards Get faster and more intuitive insights from your data using Microsoft Power BIBook DescriptionMicrosoft Power BI is a cloud-based service that helps you easily visualize and share insights using your organization's data.This book will get you started with business intelligence using the Power BI toolset, covering essential concepts such as installation,designing effective data models, as well as building basic dashboards and visualizations to make your data come to lifeYou will learn how to get your data the way you want – connecting to data sources sources and how to clean your data with the Power BI Query Editor. You will next learn how to properly design your data model to make your data easier to work with.. You will next learn how to properly design your data model to navigate table relationships and build DAX formulas to make your data easier to work with.Visualizing your data is another key element of this book, and you will learn how to follow proper data visualization styles and enhanced digital storytelling techniques.By the end of this book, you will understand how to administer your organization's Power BI environment so deployment can be made seamless, data refreshes can run properly, and security can be fully implementedWhat you will learnConnect to data sources using both import and DirectQuery optionsUse the Query Editor to apply data transformations and data cleansing processes, including learning how to write M and R scriptsDesign optimized data models by designing relationships and DAX calculationsLeverage built-in and custom visuals to design effective reportsUse the Power BI Desktop and Power BI Service to implement Row Level Security on your modelAdminister a Power BI cloud tenant for your organizationDeploy your Power BI Desktop files into the Power BI Report ServerWho this book is forThis book is for aspiring Business Intelligence professionals who want to get up and running with Microsoft Power BI. If you have a basic understanding of BI concepts and want to learn how to apply them using Microsoft Power BI, this book is for you.
R 1,693.00
