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R 580.00
Learning Generative AI Tools for Excel
Looking to take your Excel skills to the next level? With the power of AI, you can supercharge your workflows, automate repetitive tasks, and perform advanced data analysis. Written for Excel users with little to no AI experience, this book bridges the gap between traditional spreadsheet skills and the revolutionary capabilities of generative AI--empowering professionals to work smarter, faster, and with greater precision. Whether you're a data analyst, financial professional, project manager, or Excel enthusiast, this book will give you practical tutorials and actionable insights for integrating AI into your everyday work. Learn how to prepare, analyze, and visualize data using tools like ChatGPT and Copilot, and leverage AI's power to generate insights that drive better business decisions. With its step-by-step guidance, this book will be your go-to reference as you unlock a new level of productivity and analytical expertise. Learn to integrate ChatGPT and Copilot with Excel for enhanced efficiency Understand AI-driven techniques for data preparation, analysis, and visualization in Excel Discover the landscape of AI add-ins like OpenAI API and how to use them effectively Automate routine Excel tasks using generative AI for faster, more precise results Analyze data with AI tools to uncover actionable insights and drive smarter decisions
R 1,484.00
R 287.00
Generative AI on Microsoft Azure
Companies are now moving generative AI projects from the lab to production environments. To support these increasingly sophisticated applications, they're turning to advanced practices such as multiagent architectures and complex code-based frameworks. This practical handbook shows you how to leverage cutting-edge techniques using Microsoft's powerful ecosystem of tools to deploy trustworthy AI systems tailored to your organization's needs. Written for and by AI professionals, Generative AI on Microsoft Azure goes beyond the technical core aspects, examining underlying principles, tools, and practices in depth, from the art of prompt engineering to strategies for fine-tuning models to advanced techniques like retrieval-augmented generation (RAG) and agentic AI. Through real-world case studies and insights from top experts, you'll learn how to harness AI's full potential on Azure, paving the way for groundbreaking solutions and sustainable success in today's AI-driven landscape. Understand the technical foundations of generative AI and how the technology has evolved over the last few yearsImplement advanced GenAI applications using Microsoft services like Azure AI Foundry, Copilot, GitHub Models, Azure Databricks, and Snowflake on AzureLeverage patterns, tools, frameworks, and platforms to customize AI projectsManage, govern, and secure your AI-enabled systems with responsible AI practicesBuild upon expert guidance to avoid common pitfalls, future-proof your applications, and more
R 1,596.00
Deep Learning and Scientific Computing with R torch
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classificationProvide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with.Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
R 9,337.89
Generative AI for Personalized Learning
The rapid evolution of generative AI (GenAI) is reshaping education, making personalized learning more adaptive, accessible, and effective than ever before. This book explores the profound ways in which GenAI-driven tools—such as GPT-4, transformers, and GANs—are transforming traditional teaching and learning paradigms. From real-time curriculum adaptation to AI-powered tutors, GenAI is not just enhancing education—it is redefining it.Through in-depth analysis and real-world case studies from Finland, Singapore, Japan, and the United States, this book examines how AI-driven learning environments foster student engagement and improve outcomes. It highlights the shift from one-size-fits-all education to truly individualized learning paths, where AI dynamically adjusts content, pacing, and feedback to each learner’s needs. Teachers, rather than being replaced, are empowered with intelligent tools that enhance their instructional strategies.The rise of AI in education also brings critical challenges. This book does not shy away from discussing ethical concerns, data privacy issues, and the risks of bias in AI-driven education. It provides a balanced perspective, offering strategies to ensure that GenAI serves all students equitably, promoting fairness and inclusivity in the classroom. Whether you are an educator, researcher, policymaker, or technology leader, Generative AI for Personalized Learning offers valuable insights into the future of education. It presents a forward-thinking vision—ne where AI and human expertise work in harmony to create a more responsive, scalable, and learner-centric educational experience.The future of learning is not coming—t is already here. This book is your essential guide to understanding and navigating this transformation.
R 6,680.18
R 878.00
Generative AI in Teaching and Learning
Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes. The book covers a wide range of topics, including generative AI applications in artwork, coding, writing, research, collaboration, and more. It examines the implications of public generative AI interfaces and explores how generative AI can be effectively utilized in formal education settings, such as accredited institutions. The roles of teachers, content creation, instructional design, research, community service, sports, policies, assignments, grading, student work, and programming are all examined in relation to generative AI. Furthermore, the book explores the use of generative AI in nonformal and informal teaching and learning, emphasizing the opportunities for self-learning, group learning, and social change that arise from its implementation. It also delves into the professional ethics surrounding the application of generative AI in education. While examining the potential benefits of generative AI, the book also addresses the risks associated with demotivation for skill acquisition, deskilling, and downskilling. It highlights the opportunities for upskilling and encourages educators and learners to engage in learning practices that leverage the power of generative AI.
R 8,929.94
R 489.00
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
Generative AI with Amazon Bedrock
Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standardsKey FeaturesLearn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist ArchitectsMaster the core techniques to develop and deploy several AI applications at scaleGo beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricksPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure.Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock.By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learnExplore the generative AI landscape and foundation models in Amazon BedrockFine-tune generative models to improve their performanceExplore several architecture patterns for different business use casesGain insights into ethical AI practices, model governance, and risk mitigation strategiesEnhance your skills in employing agents to develop intelligence and orchestrate tasksMonitor and understand metrics and Amazon Bedrock model responseExplore various industrial use cases and architectures to solve real-world business problems using RAGStay on top of architectural best practices and industry standardsWho this book is forThis book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.
R 1,465.00
Generative Adversarial Networks Projects
Explore various Generative Adversarial Network architectures using the Python ecosystemKey FeaturesUse different datasets to build advanced projects in the Generative Adversarial Network domainImplement projects ranging from generating 3D shapes to a face aging applicationExplore the power of GANs to contribute in open source research and projectsBook DescriptionGenerative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain.Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation.By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.What you will learnTrain a network on the 3D ShapeNet dataset to generate realistic shapesGenerate anime characters using the Keras implementation of DCGANImplement an SRGAN network to generate high-resolution imagesTrain Age-cGAN on Wiki-Cropped images to improve face verificationUse Conditional GANs for image-to-image translationUnderstand the generator and discriminator implementations of StackGAN in KerasWho this book is forIf you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.
R 1,661.00
R 977.00
Natural Language Processing with PyTorchlow
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations. Explore computational graphs and the supervised learning paradigm Master the basics of the PyTorch optimized tensor manipulation library Get an overview of traditional NLP concepts and methods Learn the basic ideas involved in building neural networks Use embeddings to represent words, sentences, documents, and other features Explore sequence prediction and generate sequence-to-sequence models Learn design patterns for building production NLP systems
R 2,280.00
Building Generative AI Solutions for Industry with AWS
Building Generative AI Solutions for Industry with AWS is a comprehensive guide for developers, and data scientists aiming to harness the power of Generative AI across a range of industrial domains using Amazon Web Services. This hands-on book provides readers with the knowledge and practical skills to design, secure, govern, and deploy GenAI solutions effectively in real-world environments. The book begins with an introduction to the fundamentals of Generative AI and the AI/ML services offered by AWS. Readers will explore industry use cases and learn how to kick-start their journey building GenAI applications on the AWS cloud. It focuses on security and governance, covering essential practices for data privacy, access control, encryption, compliance, and ethical AI deployment. Each chapter then dives deep into specific industry applications--automotive, manufacturing, healthcare, financial services, and media & entertainment. From autonomous driving and predictive maintenance to fraud detection and content creation, readers will learn how GenAI is transforming operations and unlocking new opportunities in these fields. The book also addresses cost management strategies for GenAI on AWS, offering guidance on optimizing resources and budgeting efficiently. Concluding with future possibilities and implementation best practices, this book equips professionals with the tools and insights to build scalable, secure, and compliant GenAI systems tailored to their domain. What you will learn: Understand the fundamentals of Generative AI and AWS AI/ML services.How to build and deploy secure, compliant GenAI applications on AWS.Explore real-life GenAI use cases across industries like automotive, healthcare, and finance.Gain insights into managing GenAI costs and optimizing AWS resources. Who this book is for: Software developers, machine learning engineers, data scientists, and cloud architects who want to build real-life Generative AI solutions using AWS.
R 2,417.79
Unlocking Data with Generative AI and RAG
Leverage cutting-edge generative AI techniques such as RAG to realize the potential of your data and drive innovation as well as gain strategic advantageKey FeaturesOptimize data retrieval and generation using vector databasesBoost decision-making and automate workflows with AI agentsOvercome common challenges in implementing real-world RAG systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGenerative AI is helping organizations tap into their data in new ways, with retrieval-augmented generation (RAG) combining the strengths of large language models (LLMs) with internal data for more intelligent and relevant AI applications. The author harnesses his decade of ML experience in this book to equip you with the strategic insights and technical expertise needed when using RAG to drive transformative outcomes.The book explores RAG’s role in enhancing organizational operations by blending theoretical foundations with practical techniques. You’ll work with detailed coding examples using tools such as LangChain and Chroma’s vector database to gain hands-on experience in integrating RAG into AI systems. The chapters contain real-world case studies and sample applications that highlight RAG’s diverse use cases, from search engines to chatbots. You’ll learn proven methods for managing vector databases, optimizing data retrieval, effective prompt engineering, and quantitatively evaluating performance. The book also takes you through advanced integrations of RAG with cutting-edge AI agents and emerging non-LLM technologies.By the end of this book, you’ll be able to successfully deploy RAG in business settings, address common challenges, and push the boundaries of what’s possible with this revolutionary AI technique.What you will learnUnderstand RAG principles and their significance in generative AIIntegrate LLMs with internal data for enhanced operationsMaster vectorization, vector databases, and vector search techniquesDevelop skills in prompt engineering specific to RAG and design for precise AI responsesFamiliarize yourself with AI agents' roles in facilitating sophisticated RAG applicationsOvercome scalability, data quality, and integration issuesDiscover strategies for optimizing data retrieval and AI interpretabilityWho this book is forThis book is for AI researchers, data scientists, software developers, and business analysts looking to leverage RAG and generative AI to enhance data retrieval, improve AI accuracy, and drive innovation. It is particularly suited for anyone with a foundational understanding of AI who seeks practical, hands-on learning. The book offers real-world coding examples and strategies for implementing RAG effectively, making it accessible to both technical and non-technical audiences. A basic understanding of Python and Jupyter Notebooks is required.
R 1,465.00
Debugging Machine Learning Models with Python
Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey FeaturesLearn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies.By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What you will learnEnhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is forThis book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills.
R 1,693.00
R 397.00
R 305.00
R 305.00
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
Deep Learning and Scientific Computing with R torch
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++.Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold:Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classificationProvide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with.Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
R 3,135.46
Artificial Intelligence with Python
Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence.This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
R 2,015.00
R 855.00
R 577.00
Genai on Google Cloud
In today's AI landscape, success depends not just on using large language models but on moving them into production in a way that's scalable, compliant, and cost-effective. GenAI on Google Cloud: Enterprise Generative AI Systems and Agents is your hands-on guide to bridging that gap. Whether you're an ML engineer tackling your first multi-agent application or a product leader responsible for scaling a GenAI strategy, this book offers a practical game plan for taking your LLM applications from promising prototype to enterprise-grade solution. Written by practitioners with deep experience in MLOps, data engineering, and GenAI infrastructure, this guide takes you through real-world workflows using Google Cloud's Vertex AI--from training and deployment to monitoring and integration. With concrete examples, field-tested frameworks, and honest insights about what actually works, you'll learn how to build production systems that deliver measurable business value. Bridge the prototype-to-production chasm that stalls 75% of LLM projects using systematic frameworks developed across hundreds of deploymentsNavigate the unique complexities of LLM operations through practical guidance on data orchestration, evaluation frameworks, and responsible AI practicesBuild enterprise-grade multimodal systems that handle text, images, and video with robust agent architectures and tool integrationImplement proven optimization strategies for cost management, performance tuning, and production monitoring at scaleApply battle-tested patterns from practitioners who've guided organizations through both breakthrough successes and costly failures
R 1,824.00
Generative Adversarial Networks Cookbook
Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and KerasKey FeaturesUnderstand the common architecture of different types of GANsTrain, optimize, and deploy GAN applications using TensorFlow and KerasBuild generative models with real-world data sets, including 2D and 3D dataBook DescriptionDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use.By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.What you will learnStructure a GAN architecture in pseudocodeUnderstand the common architecture for each of the GAN models you will buildImplement different GAN architectures in TensorFlow and KerasUse different datasets to enable neural network functionality in GAN modelsCombine different GAN models and learn how to fine-tune themProduce a model that can take 2D images and produce 3D modelsDevelop a GAN to do style transfer with Pix2PixWho this book is forThis book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.
R 1,661.00
R 489.00
Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 880.00
Generative AI 2.0 and Data Analytics
Data analytics and generative AI (Gen AI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.Generative AI 2.0 and Data Analytics explores the intersection between Gen AI and data analytics and addresses its profound efects on industries and organizations across the globe. Highlights of the book include:Deep learning architectures for generative models in business data managementOptimizing human-AI collaboration for strategic decision-making in business practisesBenchmarking practices and evaluation metrics for Generative AI in business data analyticsNot only covering the fundamental concepts, and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.
R 8,572.00
R 1,301.00
Generative AI for Students
Using Generative Artificial Intelligence as a student can be confusing and challenging. This authoritative guide equips you with practical knowledge, enabling you to leverage Generative AI as a powerful study aid and embark on your academic journey with confidence. Exploring a wide spectrum of topics, from demystifying jargon and fostering critical thinking to addressing diversity, inclusion and academic integrity, this book is an essential companion for any Higher Education student looking to enhance their academic skill set and achieve success in their studies. By the end of this book you will:- Become critically literate at using Generative AI- Learn how to use Generative AI to enhance traditional study methods- Encounter real-life case studies and worked-through examples to put theory into practice- Gain a comprehensive grounding in how to engage with Generative AI Tools
R 3,438.80
Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
R 4,559.00
R 1,313.00
ML and Generative AI in the Data Lakehouse
In today's race to harness generative AI, many teams struggle to integrate these advanced tools into their business systems. While platforms like GPT-4 and Google's Gemini are powerful, they aren't always tailored to specific business needs. This book offers a practical guide to building scalable, customized AI solutions using the full potential of data lakehouse architecture.Author Bennie Haelen covers everything from deploying ML and GenAI models in Databricks to optimizing performance with best practices. In this must-read for data professionals, you'll gain the tools to unlock the power of large language models (LLMs) by seamlessly combining data engineering and data science to create impactful solutions.Learn to build, deploy, and monitor ML and GenAI models on a data lakehouse architecture using DatabricksLeverage LLMs to extract deeper, actionable insights from your business data residing in lakehousesDiscover how to integrate traditional ML and GenAI models for customized, scalable solutionsUtilize open source models to control costs while maintaining model performance and efficiencyImplement best practices for optimizing ML and GenAI models within the Databricks platform
R 1,978.00
R 305.00
Teaching and Learning in Higher Education with Generative AI
This interdisciplinary book demonstrates how Generative Artificial Intelligence (GenAI) changes higher education across diverse academic fields, including philology, management, engineering, and psychology. Drawing on empirical case studies, surveys, and experiments, the book combines theoretical and practical perspectives to explore AI’s impact on learning, and cognitive development. Each chapter offers discipline-specific analysis of how GenAI reshapes pedagogy – supporting multilingual education, enhancing reflective thinking, fostering self-regulated learning, and encouraging ethical awareness. By comparing case studies from varied academic and cultural contexts, the book highlights the opportunities and challenges of AI adoption, emphasizing responsible, creative, and critically informed approaches to academic innovation. It will appeal to researchers of AI in Higher Education across disciplines.Key Features:- Explains how Generative AI transforms higher education across a variety of disciplines, from logistics to psychology.- Includes practical and theoretical insights into AI’s role in reshaping pedagogy, fostering critical thinking, and supporting multilingual education.- Provides a global perspective on the opportunities and challenges of AI adoption through diverse case studies and empirical research.
R 3,297.00
Generative Adversarial Networks in Practice
Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts. Key features: Guides you through the complex world of GANs, demystifying their intricacies. Accompanies your learning journey with real-world examples and practical applications. Navigates the theory behind GANs, presenting it in an accessible and comprehensive way. Simplifies the implementation of GANs using popular deep learning platforms. Introduces various GAN architectures, giving readers a broad view of their applications. Nurture your knowledge of AI with our comprehensive yet accessible content. Practice your skills with numerous case studies and coding examples. Reviews advanced GANs such as DCGAN, CGAN, CycleGAN, and more, with clear explanations and practical examples. Adapts to both beginners and experienced practitioners, with content organized to cater to varying levels of familiarity with GANs. Connects the dots between GAN theory and practice, providing a well-rounded understanding of the subject. Takes you through GAN applications across different data types, highlighting their versatility. Inspires the reader to explore beyond the book, fostering an environment conducive to independent learning and research. Closes the gap between complex GAN methodologies and their practical implementation, allowing readers to directly apply their knowledge. Empowers you with the skills and knowledge needed to confidently use GANs in your projects.Prepare to deep dive into the captivating realm of GANs and experience the power of AI like never before with Generative Adversarial Networks (GANs) in Practice. This book brings together the theory and practical aspects of GANs in a cohesive and accessible manner, making it an essential resource for both beginners and experienced practitioners.
R 4,313.79
Generative AI for Full-Stack Development
Gain cutting-edge skills in building a full-stack web application with AI assistance. This book will guide you in creating your own travel application using React and Node.js, with MongoDB as the database, while emphasizing the use of Gen AI platforms like Perplexity.ai and Claude for quicker development and more accurate debugging. The book's step-by-step approach will help you bridge the gap between traditional web development methods and modern AI-assisted techniques, making it both accessible and insightful. It provides valuable lessons on professional web application development practices. By focusing on a practical example, the book offers hands-on experience that mirrors real-world scenarios, equipping you with relevant and in-demand skills that can be easily transferred to other projects. The book emphasizes the principles of responsive design, teaching you how to create web applications that adapt seamlessly to different screen sizes and devices. This includes using fluid grids, media queries, and optimizing layouts for usability across various platforms. You will also learn how to design, manage, and query databases using MongoDB, ensuring you can effectively handle data storage and retrieval in your applications. Most significantly, the book will introduce you to generative AI tools and prompt engineering techniques that can accelerate coding and debugging processes. This modern approach will streamline development workflows and enhance productivity. By the end of this book, you will not only have learned how to create a complete web application from backend to frontend, along with database management, but you will also have gained invaluable associated skills such as using IDEs, version control, and deploying applications efficiently and effectively with AI. What You Will Learn How to build a full-stack web application from scratchHow to use generative AI tools to enhance coding efficiency and streamline the development processHow to create user-friendly interfaces that enhance the overall experience of your web applicationsHow to design, manage, and query databases using MongoDB Who This Book Is For Frontend developers, backend developers, and full-stack developers.
R 1,254.00
