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Intermediate Python and Large Language Models
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you'll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You'll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. By the end of the book, you'll have gained a solid understanding of LLM architectures, hands-on experience with LangChain, and the expertise to build scalable AI applications that redefine human-computer interaction. What You Will Learn Understand the fundamentals of LangChain and Python for LLM development Know advanced AI workflows, including fine-tuning and memory management Build AI-powered applications such as chatbots, retrieval systems, and automation tools Know deployment strategies and performance optimization for real-world use Use best practices for scalability, security, and responsible AI implementation Unlock the full potential of LLMs and take your AI development skills to the next level Who This Book Is For Software engineers and Python developers interested in learning the foundations of LLMs and building advanced modern LLM applications for various tasks
R 1,254.00
Explainable AI with Python
This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expansion of the "Intrinsic Explainable Models" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models. Further details in "Model-Agnostic Methods for XAI" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively. New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface. Revision in "Adversarial Machine Learning and Explainability" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices. New chapter on "Generative Models and Large Language Models (LLM)" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models. New "Artificial General Intelligence and XAI" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI. Enhancements in "Explaining Deep Learning Models" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.
R 2,198.00
Introduction to Python and Large Language Models
Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming.The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components.You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots.In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs.What You’ll LearnUnderstand the basics of Python and the features of Python 3.11Explore the essentials of NLP and how do they lay the foundations for LLMs.Review LLM components.Develop basic apps using LLMs and Python.Who This Book Is ForData analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.
R 2,412.29
Deep Reinforcement Learning with Python
Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning covers how multiple agents compete, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO). You'll see how reinforcement learning with human feedback (RLHF) has been used by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it’s for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.What You'll LearnExplore Python-based RL libraries, including StableBaselines3 and CleanRL Work with diverse RL environments like Gymnasium, Pybullet, and Unity MLUnderstand instruction finetuning of Large Language Models using RLHF and PPOStudy training and optimization techniques using HuggingFace, Weights and Biases, and Optuna Who This Book Is ForSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch.
R 1,368.00
R 1,301.00
Building AI Systems with Python
This book is a practical, end-to-end guide for engineers and practitioners who want to move beyond prototypes and confidently deploy machine learning and large language model solutions in real-world environments. This book guides through the entire modern machine learning lifecycle. You'll start with foundations using NumPy, Pandas, and PyArrow for data pipelines, then build solid baselines with scikit-learn. From there, you advance into deep learning with PyTorch, transformers, and LLM adaptation techniques such as LoRA and QLoRA. You'll explore diffusion and multimodal models, and learn to build retrieval-augmented generation systems with FAISS and pgvector. Practical chapters cover agents, tool use, evaluation frameworks, observability, and responsible AI practices including privacy, safety, and governance. Finally, you'll master deployment techniques using FastAPI, Ray Serve, TorchServe, and cutting-edge LLM serving engines like vLLM and TGI. Each concept is paired with clear code examples, testing patterns, and operational checklists. Instead of one-off tricks, you'll adopt repeatable workflows: schema-first tooling, reproducible training pipelines, evaluation with golden datasets, and secure production rollouts with monitoring and compliance checkpoints. In the end, this book helps you build systems that are robust, auditable, and optimized--whether you're deploying your first model or managing complex enterprise workloads. For engineers who want to ship AI confidently and responsibly, this is your practical playbook for the GenAI era. What you will learn: Implement modern AI models including transformers, diffusion, multimodal, recommenders, and RL using practical PyTorch examples. Fine tune and serve LLMs with LoRA/QLoRA, quantization, RAG, tool calling, structured prompts, and robust evaluation techniques. Design agentic AI systems with memory, planning, safe tool execution, multi agent patterns, and autonomy evaluation frameworks. Deploy and run production grade AI with MLOps/LLMOps covering serving, performance tuning, monitoring, cost control, compliance, and edge deployments. Who this book is for: This book is designed for practicing machine learning and AI engineers, software engineers moving into applied AI, data scientists building production systems, MLOps/LLMOps practitioners, and technical builders who want to go beyond demos and deploy real-world GenAI, LLM, and PyTorch-based systems at scale.
R 1,368.00
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll LearnReview data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithmUnderstand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networksGet acquainted with scikit-learn and PyTorchPredict sequences in recurrent neural networks and long short term memory Who This Book Is ForData scientists, machine learning engineers, and software professionals with basic skills in Python programming.
R 1,482.00
Advanced Forecasting with Python
Advanced Forecasting with Python, Second Edition, is a comprehensive and practical guide to mastering modern forecasting techniques using Python. Designed for data scientists, analysts, and machine learning practitioners, this updated edition bridges the gap between classical forecasting models and cutting-edge, AI-powered techniques that are reshaping the field. The book begins with foundational models like AR, MA, ARIMA, and SARIMA, offering intuitive and mathematical explanations alongside hands-on Python implementations. It then expands into multivariate models (VAR, VARMAX), supervised machine learning (Random Forests, XGBoost, LightGBM, CatBoost), and deep learning architectures such as LSTMs, NBEATS, and Transformers. Each chapter not only teaches the theory and code but also tracks model performance using MLflow, enabling efficient benchmarking and experimentation management. The second edition stands out for its extensive new content. Readers will now explore Orbit by Uber, AutoGluon by AWS, Prophet by Meta, Microsoft Azure AutoML, Google GCP AutoML, and TimeGPT by Nixtla, equipping them with the latest tools from top cloud providers. These additions make sure that readers stay current in an ever-evolving landscape. Moreover, the new chapters highlight practical deployment strategies and trade-offs between performance, explainability, and scalability. Whether you are just beginning your forecasting journey or seeking to enhance your expertise with state-of-the-art tools and cloud-based solutions, this book offers a rich, hands-on learning experience. With step-by-step Python examples, detailed model insights, and modern forecasting workflows, it is an indispensable resource for staying ahead in the realm of predictive analytics. You Will: Build robust forecasting solutions using PythonGain both intuitive and mathematical insights into traditional and cutting-edge forecasting models Master model evaluation through cross-validation, backtesting, and MLflow-based trackingLeverage cloud-based platforms and Model-as-a-Service tools for scalable forecasting deployments Who this book is for: This book is ideal for data scientists, analysts, and ML practitioners working on real-world forecasting problems. It suits both intermediate learners and experienced professionals looking to master state-of-the-art forecasting techniques.
R 912.00
Prototyping Python Dashboards for Scientists and Engineers
Create interactive and data-driven dashboards using Python. This hands-on guide is a practical resource for those (with modest programming skills) in scientific and engineering fields looking to leverage Python's power for data visualization and analysis in a user-friendly dashboard format.You’ll begin by gaining a fundamental understanding of Python programming, including data types, lists, dictionaries, and data structures. The book then delves into the world of reactive programming with Plotly and Dash, offering a hands-on approach to building interactive web-based dashboards. Next, you’ll see how to work with online data, how to scrape and clean datasets, and keep files up-to-date. The book also guides you through planning a dashboard prototype, outlining project tasks, trends, forecasts, spectra, and other design considerations. It concludes with a discussion of how the dashboard can be used for data visualization of real data, explaining the usefulness of tools such as spectra.By providing detailed examples for download and customization, Prototyping Python Dashboards for Scientists and Engineers will equip you with the skills needed to jumpstart your own development efforts.What You’ll LearnDesign a dashboard with PythonConvert and filter Excel formatted files to produce CSV filesCreate browser-served graphics with PLOTLYGenerate polynomial trend lines for forecastingBuild a Unix service to share your dashboardWho This Book Is ForScientists, engineers, students, programmers, and data enthusiasts who aspire to harness Python's potential for data visualization and analysis through the creation of interactive dashboards. Many will be pragmatic programmers with modest skills and limited resources who mainly want to see a working solution they could emulate.
R 1,026.00
Applied Recommender Systems with Python
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.
R 1,161.66
Adaptive Machine Learning Algorithms with Python
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use.Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment.What You Will LearnApply adaptive algorithms to practical applications and examplesUnderstand the relevant data representation features and computational models for time-varying multi-dimensional dataDerive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real dataSpeed up your algorithms and put them to use on real-world stationary and non-stationary dataMaster the applications of adaptive algorithms on critical edge device computation applicationsWho This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.
R 1,026.00
R 1,628.00
Large Language Models Projects
This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face libraryUse essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databasesCreate and implement projects using LLM while understanding the design decisions involvedUnderstand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks
R 1,482.00
Mastering LangChain
This book provides a comprehensive exploration of LangChain, empowering you to effectively harness large language models (LLMs) for Gen AI applications. It focuses on practical implementation and techniques, making it a valuable resource for learning LangChain. The book starts with foundational topics such as environment setup and building basic chains, then delves into key components such as prompt templates, tool integration, and memory management. You will also explore practical topics such as output parsing, embedding models, and developing chatbots and retrieval-augmented generation (RAG) systems. Additional chapters focus on integrating LangChain with other AI tools and deploying applications while emphasizing best practices for AI ethics and performance. By the time you finish this book, you'll have the know-how to confidently build Generative AI solutions using LangChain. Whether you're exploring practical applications or curious about the latest trends, this guide gives you the tools and insights to solve real-world AI problems. You'll be ready to design smart, data-driven applications--and rethink how you approach Generative AI. What You Will Learn Understand the core ideas, architecture, and essential features of the LangChain framework Create advanced LLM-driven workflows and applications that address real-world challengesDevelop robust Retrieval-Augmented Generation (RAG) systems using LangChain, vector databases, and proven best practices for retrieving and generating high-quality responses Who This Book Is For Data scientists and AI enthusiasts with basic Python skills who want to use LangChain for advanced development, and Python developers interested in building data-responsive applications with large language models (LLMs)
R 1,254.00
Large Language Model Recipes
Large Language Models Recipes is a comprehensive guide designed to help developers, and AI practitioners navigate the complexities of working with LLMs. It explains fine-tuning open-source models and deploying scalable AI solutions, providing practical insights and hands-on examples in a recipe-style format for easy understanding and application. The book begins with a step-by-step guide to setting up an efficient development environment, covering hardware considerations, cloud services, and essential tools like PyTorch and TensorFlow. It then introduces readers to open-source language models, offering guidance on selecting and loading models such as GPT-J, LLaMA, and Falcon. It has dedicated chapters exploring fine-tuning, transfer learning, and quantization techniques to optimize performance. Readers will also discover advanced topics, including model distillation, deployment strategies on cloud platforms like AWS and GCP, and efficient data handling methods. Additionally, it covers scaling down large models for limited-resource environments, monitoring and debugging techniques, and integrating external tools such as vector databases for Retrieval-Augmented Generation (RAG). By the end of this book, readers will have a solid foundation in working with LLMs--from setting up their environment to deploying efficient, scalable AI solutions. With practical recipes, real-world applications, and cutting-edge techniques, Large Language Models Recipes is an essential resource for anyone looking to harness the full potential of LLMs in modern AI workflows. What you will learn: How to configure hardware, install essential tools, and optimize workflows for working with LLMs.Explore techniques like fine-tuning, quantization, and model distillation for efficient performance.Explore deployment strategies, cloud platforms, and edge computing for real-world applications.Understand multimodal LLMs, Retrieval-Augmented Generation (RAG), and external tool integrations. Who this book is for: This book is ideal for data scientists, machine learning engineers, and AI enthusiasts looking to understand and develop Large Language Models and their applications.
R 1,368.00
Understanding Generative AI Business Applications
This guide covers the fundamental technical principles and various business applications of Generative AI for planning, developing, and evaluating AI-driven products. It equips you with the knowledge you need to harness the potential of Generative AI for enhancing business creativity and productivity.The book is organized into three sections: text-based, senses-based, and rationale-based. Each section provides an in-depth exploration of the specific methods and applications of Generative AI. In the text-based section, you will find detailed discussions on designing algorithms to automate and enhance written communication, including insights into the technical aspects of transformer-based Natural Language Processing (NLP) and chatbot architecture, such as GPT-4, Claude 2, Google Bard, and others. The senses-based section offers a glimpse into the algorithms and data structures that underpin visual, auditory, and multisensory experiences, including NeRF, 3D Gaussian Splatting,Stable Diffusion, AR and VR technologies, and more. The rationale-based section illuminates the decision-making capabilities of AI, with a focus on machine learning and data analytics techniques that empower applications such as simulation models, agents, and autonomous systems.In summary, this book serves as a guide for those seeking to navigate the dynamic landscape of Generative AI. Whether you’re a seasoned AI professional or a business leader looking to harness the power of creative automation, these pages offer a roadmap to leverage Generative AI for your organization’s success.What You Will LearnWhat are the technical elements that constitute the makeup of Generative AI products?What are the practical applications of Generative AI?How can algorithms be designed to automate and improve written communication?What are the latest Generative AI architectures and algorithms?Who This Book Is ForData scientists, data analysts, decision makers, and business executives interested in gaining an understanding of Generative AI products
R 1,254.00
Generative Artificial Intelligence
This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models.The book begins with an introduction to the foundations of Generative AI, including an overview of the field, its evolution, and its significance in today’s AI landscape. It focuses on generative visual models, exploring the exciting field of transforming text into images and videos. A chapter covering text-to-video generation provides insights into synthesizing videos from textual descriptions, opening up new possibilities for creative content generation. A chapter covers generative audio models and prompt-to-audio synthesis using Text-to-Speech (TTS) techniques. Then the book switch gears to dive into generative text models, exploring the concepts of Large Language Models (LLMs), natural language generation (NLG), fine-tuning, prompt tuning, and reinforcement learning. The book explores techniques for fixing LLMs and making them grounded and indestructible, along with practical applications in enterprise-grade applications such as question answering, summarization, and knowledge-based generation.By the end of this book, you will understand Generative text, and audio and visual models, and have the knowledge and tools necessary to harness the creative and transformative capabilities of Generative AI. What You Will LearnWhat is Generative Artificial Intelligence?What are text-to-image synthesis techniques and conditional image generation?What is prompt-to-audio synthesis using Text-to-Speech (TTS) techniques?What are text-to-video models and how do you tune them?What are large language models, and how do you tune them? Who This Book Is ForThose with intermediate to advanced technical knowledge in artificial intelligence and machine learning
R 1,140.00
Time Series Forecasting Using Generative AI
"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies."The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs.This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights.● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting.● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting.● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting.● Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM.● Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions.Who this book is for:Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
R 1,832.00
Transformation Enterprise Architecture with Agentic AI
This book is a practical guide to transforming enterprise architecture in the age of generative AI. As intelligent systems reshape how organizations build, scale, and govern technology, this book bridges the gap between traditional architectural frameworks and the emerging GenAI-driven landscape.The book begins with an overview of enterprise architecture principles, including TOGAF, Zachman, and Gartner frameworks, and dives into core architectural layers—application, data, technology, infrastructure, and security. It then introduces the fundamentals of Generative AI, its model types, and its rapid evolution within enterprise contexts. The core of the book is how GenAI disrupts and elevates architectural thinking—from conversational modeling and automated decision records to reimagining design lifecycles through intelligent automation, augmentation, and autonomy. Readers will learn how to modernize application portfolios, document and repair APIs, and embed LLMs as intelligent middleware. Dedicated chapters explore GenAI's transformative impact on platform engineering, DevOps, cloud/edge infrastructure, observability, and AI-augmented governance. Key use cases include auto-generating code and configurations, orchestrating deployment pipelines with non-human agents, enabling self-documenting platforms, and enhancing security through GenAI-driven threat modeling and compliance enforcement. The book concludes by examining responsible AI practices and human oversight in autonomous architectures, offering practical insights into creating secure, scalable, and resilient systems powered by GenAI.Whether you're an architect or developer, this book provides a modern framework for applying GenAI across the enterprise stack and designing the next generation of intelligent, adaptive, and self-governing enterprise systems. What you will learn:Understand core enterprise architecture principles and frameworks in the context of a generative AI-driven world.How GenAI is disrupting and enhancing every layer of enterprise architecture—from design to deployment.Explore practical methods for integrating GenAI into application architecture, API ecosystems, and DevOps pipelines.Discover how intelligent agents, automation, and orchestration are reshaping platform engineering and cloud infrastructure. Who this book is for:This book is for enterprise architects, AI practitioners and machine learning engineers seeking to integrate Generative AI into enterprise systems.
R 1,368.00
