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Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable.\nFrom NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence.\nYou’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models.\nWhether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI.\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eGrasp the core concepts and capabilities of LLMs\u003c\/li\u003e\n\u003cli\u003eCraft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs\u003c\/li\u003e\n\u003cli\u003eUnderstand how attention and transformers have changed NLP\u003c\/li\u003e\n\u003cli\u003eOptimize your diffusion models by combining them with VAEs\u003c\/li\u003e\n\u003cli\u003eBuild text generation pipelines based on LSTMs and LLMs\u003c\/li\u003e\n\u003cli\u003eLeverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eWho this book is for\u003c\/h4\u003eThis book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596261855518,"sku":"9781835884454","price":43.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_e20a9718-07a5-4b1c-8f81-569151f055aa.jpg?v=1770652890"},{"product_id":"openai-gpt-for-python-developers-9781836202400","title":"OpenAI GPT For Python Developers","description":"\u003cp\u003e\u003cb\u003e\"OpenAI GPT for Python Developers\" is your comprehensive guide to mastering the integration of OpenAI's GPT models into your Python projects, enhancing applications with various AI capabilities from chat completions to AI avatars.\u003c\/b\u003e\u003c\/p\u003e\u003ch4\u003eKey Features\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eStrategies for optimizing and personalizing GPT models for specific applications.\u003c\/li\u003e\n\u003cli\u003eInsights into integrating additional OpenAI technologies like Whisper and Weaviate.\u003c\/li\u003e\n\u003cli\u003eStrong emphasis on responsible AI development and deployment.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eBook Description\u003c\/h4\u003e“OpenAI GPT for Python Developers” is meticulously crafted to provide Python developers with a deep dive into the mechanics and applications of GPT technology, beginning with a captivating narrative on the evolution of OpenAI and the fundamental workings of GPT models. As readers progress, they will be expertly guided through the essential steps of setting up a development environment tailored for AI innovations, coupled with insightful advice on selecting the most appropriate GPT model to suit specific project needs.\nThe guide progresses into practical tutorials that cover the implementation of chat completions and the art of prompt engineering, providing a solid foundation in harnessing the capabilities of GPT for generating human-like text responses. Practical applications are further expanded with discussions on the creation of autonomous AI-to-AI dialogues, the development of AI avatars, and the strategic use of AI in interactive applications.\nIn addition to technical skills, this book addresses the ethical implications and prospects of AI technologies, encouraging a holistic view of AI development. The guide is enriched with detailed examples, step-by-step tutorials, and comprehensive explanations that illuminate the theoretical aspects and emphasize practical implementation.\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eSet up the development environment for OpenAI GPT.\u003c\/li\u003e\n\u003cli\u003eUnderstand and choose the right GPT model for your needs.\u003c\/li\u003e\n\u003cli\u003eImplement advanced prompt engineering techniques.\u003c\/li\u003e\n\u003cli\u003eExplore embedding and advanced embedding examples.\u003c\/li\u003e\n\u003cli\u003eUtilize OpenAI's Whisper for speech recognition and translation.\u003c\/li\u003e\n\u003cli\u003eIntegrate OpenAI TTS models for text-to-speech applications.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eWho this book is for\u003c\/h4\u003eThis book is designed for readers at an intermediate to advanced level who have a basic understanding of machine learning concepts and are eager to expand their expertise in AI with a focus on OpenAI's technologies. Ideal for those involved in AI-driven projects, the book assumes familiarity with Python programming and a fundamental grasp of AI principles. It’s especially beneficial for developers aiming to integrate GPT models into applications, AI researchers, and technical professionals involved in AI product development.","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596266213662,"sku":"9781836202400","price":19.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_2a424adb-1775-444c-a119-73bc500265ee.jpg?v=1770652897"},{"product_id":"building-ai-intensive-python-applications-9781836207245","title":"Building AI Intensive Python Applications","description":"\u003cp\u003e\u003cb\u003eMaster retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps\u003c\/b\u003e\u003c\/p\u003e\u003ch4\u003eKey Features\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eGet to grips with the fundamentals of LLMs, vector databases, and Python frameworks\u003c\/li\u003e\n\u003cli\u003eImplement effective retrieval-augmented generation strategies with MongoDB Atlas\u003c\/li\u003e\n\u003cli\u003eOptimize AI models for performance and accuracy with model compression and deployment optimization\u003c\/li\u003e\n\u003cli\u003ePurchase of the print or Kindle book includes a free PDF eBook\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eBook Description\u003c\/h4\u003eThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications.\nThe chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance.\nBy the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the architecture and components of the generative AI stack\u003c\/li\u003e\n\u003cli\u003eExplore the role of vector databases in enhancing AI applications\u003c\/li\u003e\n\u003cli\u003eMaster Python frameworks for AI development\u003c\/li\u003e\n\u003cli\u003eImplement Vector Search in AI applications\u003c\/li\u003e\n\u003cli\u003eFind out how to effectively evaluate LLM output\u003c\/li\u003e\n\u003cli\u003eOvercome common failures and challenges in AI development\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eWho this book is for\u003c\/h4\u003eThis book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596266901790,"sku":"9781836207245","price":59.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_be76ae45-d42f-425a-a97d-3a9199b08a1e.jpg?v=1770652899"},{"product_id":"generative-deep-learning-with-python-9781836207122","title":"Generative Deep Learning with Python","description":"\u003cp\u003e\u003cb\u003eDive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, \u0026amp; autoregressive models through projects \u0026amp; advanced topics. Gain practical skills \u0026amp; theoretical knowledge to create groundbreaking AI applications.\u003c\/b\u003e\u003c\/p\u003e\u003ch4\u003eKey Features\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eComprehensive coverage of deep learning and generative models.\u003c\/li\u003e\n\u003cli\u003eIn-depth exploration of GANs, VAEs, \u0026amp; autoregressive models \u0026amp; advanced topics in generative AI.\u003c\/li\u003e\n\u003cli\u003ePractical coding exercises \u0026amp; interactive assignments to build your own generative models.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eBook Description\u003c\/h4\u003eGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation.\n\nThe course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field.\n\nThroughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.\u003ch4\u003eWhat you will learn\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eDevelop a detailed understanding of deep learning fundamentals\u003c\/li\u003e\n\u003cli\u003eImplement and train Generative Adversarial Networks (GANs)\u003c\/li\u003e\n\u003cli\u003eCreate \u0026amp; utilize Variational Autoencoders for data generation\u003c\/li\u003e\n\u003cli\u003eApply autoregressive models for text generation\u003c\/li\u003e\n\u003cli\u003eExplore advanced topics \u0026amp; stay ahead in the field of generative AI\u003c\/li\u003e\n\u003cli\u003eAnalyze and optimize the performance of generative models\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch4\u003eWho this book is for\u003c\/h4\u003eThis course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596267163934,"sku":"9781836207122","price":34.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_7bce32ad-d99b-487e-a109-cf9fdfe91b2a.jpg?v=1770652900"},{"product_id":"explainable-ai-with-python-9783031922299","title":"Explainable AI with Python","description":"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.","brand":"ThinkEDU","offers":[{"title":"90 Day Option","offer_id":50596267720990,"sku":"9783031922299R90","price":30.0,"currency_code":"USD","in_stock":true},{"title":"Perpetual","offer_id":50596267753758,"sku":"9783031922299","price":59.99,"currency_code":"USD","in_stock":true},{"title":"30 Day Option","offer_id":50596267786526,"sku":"9783031922299R30","price":18.0,"currency_code":"USD","in_stock":true},{"title":"60 Day Option","offer_id":50596267819294,"sku":"9783031922299R60","price":24.0,"currency_code":"USD","in_stock":true},{"title":"120 Day Option","offer_id":50596267852062,"sku":"9783031922299R120","price":36.0,"currency_code":"USD","in_stock":true},{"title":"180 Day Option","offer_id":50596267884830,"sku":"9783031922299R180","price":39.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_3bdd0745-1d4c-4233-a372-a52288ec78a9.jpg?v=1770652902"},{"product_id":"ultimate-deepfake-detection-using-python-9788197953422","title":"Ultimate Deepfake Detection Using Python","description":"Deepfake Detection Unlocked: Python Approaches for Deepfake Images, Videos, Audio Detection.\n\nKey Features\n● Comprehensive and graded approach to Deepfake detection using Python and its libraries.\n● Practical implementation of deepfake detection techniques using Python.\n● Hands-on chapters for detecting deepfake images, videos, and audio.\n● Covers Case study for providing real-world application of deepfake detection.\n\nBook Description\nIn today's digital world, mastering deepfake detection is crucial, with deepfake content increasing by 900% since 2019 and 96% used for malicious purposes like fraud and disinformation. \"Ultimate Deepfake Detection with Python\" equips you with the skills to combat this threat using Python’s AI libraries, offering practical tools to protect digital security across images, videos, and audio.\n\nThis book explores generative AI and deepfakes, giving readers a clear understanding of how these technologies work and the challenges of detecting them. With practical Python code examples, it provides the tools necessary for effective deepfake detection across media types like images, videos, and audio. Each chapter covers vital topics, from setting up Python environments to using key datasets and advanced deep learning techniques.\n\nPerfect for researchers, developers, and cybersecurity professionals, this book enhances technical skills and deepens awareness of the ethical issues around deepfakes. Whether building new detection systems or improving current ones, this book offers expert strategies to stay ahead in digital media security.\n\nWhat you will learn\n● Understand the fundamentals of generative AI and deepfake technology and the potential risks they pose.\n● Explore the various methods and techniques used to identify deepfakes, as well as the obstacles faced in this field.\n● Learn to use essential datasets and label image, video, and audio data for building deepfake detection models\n● Apply advanced machine learning models like CNNs, RNNs, GANs, and Transformers for deepfake detection\n● Master active and passive methods for detecting face manipulation and build CNN-based image detection systems\n● Detect manipulations in videos, develop a detection system, and evaluate its performance using key metrics\n● Build and implement a practical deepfake detection system to understand how these techniques are applied in real-world scenarios.\n\nTable of Contents\n1. Introduction to Generative AI and Deepfake Technology\n2. Deepfake Detection Principles and Challenges\n3. Ethical Considerations with the Use of Deepfakes\n4. Setting Up your Machine for Deepfake Detection using Python\n5. Deepfake Datasets\n6. Techniques for Deepfake Detection\n7. Detection of Deepfake Images\n8. Detection of Deepfake Video\n9. Detection of Deepfake Audio\n10. Case Study in Deepfake Detection\nIndex\n\nAbout the Authors\nDr. Nimrita Koul is an Associate Professor of Computer Science and Engineering at Reva University in Bangalore, Karnataka, India. With a PhD in Machine Learning and an academic and research career spanning over 19 years, she is an active researcher in the areas of Machine Learning, Natural Language Processing, and Generative AI.\n\nDr. Koul is a senior member of IEEE and a member of ACM, and she has been the principal investigator for multiple research projects worth over 1.3 crores, funded by the Department of Science and Technology, Government of India. Her expertise has been recognized through several prestigious awards, including the Research Accelerator Award in 2021, the Jetson Nano Grant in 2020, and the IBM Generative AI Award in 2023.","brand":"ThinkEDU","offers":[{"title":"Perpetual","offer_id":50596267950366,"sku":"9788197953422","price":24.95,"currency_code":"USD","in_stock":true},{"title":"30 Day Option","offer_id":50596267983134,"sku":"9788197953422R30","price":8.74,"currency_code":"USD","in_stock":true},{"title":"60 Day Option","offer_id":50596268015902,"sku":"9788197953422R60","price":9.98,"currency_code":"USD","in_stock":true},{"title":"120 Day Option","offer_id":50596268048670,"sku":"9788197953422R120","price":11.23,"currency_code":"USD","in_stock":true},{"title":"180 Day Option","offer_id":50596268081438,"sku":"9788197953422R180","price":12.48,"currency_code":"USD","in_stock":true},{"title":"365 Day Option","offer_id":50596268114206,"sku":"9788197953422R365","price":18.72,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_8074421c-4d44-4011-a89e-858718bbb893.jpg?v=1770652902"},{"product_id":"beginning-python-3-with-grok-2-9781501522161","title":"Beginning Python 3 with Grok 2","description":"This book is a comprehensive guide designed to teach the fundamentals of Python programming while introducing the exciting possibilities of Generative AI. Whether you’re a novice or a developer looking to integrate Grok into your workflow, this book offers a clear, step-by-step path to mastering Python and leveraging AI-driven code generation. It begins by covering Python fundamentals, including data types, string manipulation, loops, conditional logic, and exception handling. It then introduces Python collections, such as lists, dictionaries, and sets, along with their practical applications. Readers will explore essential Python libraries like NumPy and Pandas, learning how to manipulate data and perform advanced operations. The last two chapters cover Generative AI and Grok, distinguishing it from conversational AI and provides hands-on examples of Grok-generated Python code to solve various programming tasks. Readers will find a balanced mix of theory, practical examples, and Grok-generated code to build both foundational programming skills and an understanding of AI-driven development. FEATURES • Covers Python programming basics and popular libraries like NumPy and Pandas, with a focus on practical applications • Introduces Generative AI concepts and Grok, showcasing its use in generating Python code • Includes companion files with code and images -- available from the publisher for downloading (with proof of purchase)","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596271128862,"sku":"9781501522161","price":54.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_9c5bf541-d74e-4445-aee7-7991ff986127.jpg?v=1770652909"},{"product_id":"hands-on-ai-trading-with-python-quantconnect-and-aws-9781394267668","title":"Hands-On AI Trading with Python, QuantConnect, and AWS","description":"\u003cp\u003e\u003cb\u003eMaster the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eHands-On AI Trading with Python, QuantConnect, and AWS\u003c\/i\u003e explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.\u003c\/p\u003e  \u003cp\u003eUnlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.\u003c\/p\u003e  \u003cp\u003eThe book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:\u003c\/p\u003e  \u003cul\u003e  \u003cli\u003eExamples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.\u003c\/li\u003e  \u003cli\u003eUse principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.\u003c\/li\u003e  \u003cli\u003ePredict market volatility regimes and allocate funds accordingly.\u003c\/li\u003e  \u003cli\u003ePredict daily returns of tech stocks using classifiers.\u003c\/li\u003e  \u003cli\u003eForecast Forex pairs' future prices using Support Vector Machines and wavelets.\u003c\/li\u003e  \u003cli\u003ePredict trading day momentum or reversion risk using TensorFlow and temporal CNNs.\u003c\/li\u003e  \u003cli\u003eApply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.\u003c\/li\u003e  \u003cli\u003ePerform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.\u003c\/li\u003e  \u003cli\u003eBetter Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.\u003c\/li\u003e  \u003cli\u003eAI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.\u003c\/li\u003e  \u003c\/ul\u003e  \u003cp\u003eWritten by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with \u003ci\u003eHands-On AI Trading with Python, QuantConnect, and AWS\u003c\/i\u003e.\u003c\/p\u003e","brand":"ThinkEDU","offers":[{"title":"Default Title","offer_id":50596273226014,"sku":"9781394267668","price":33.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_3708fea6-616e-41f3-8d27-4b3a0c49698d.jpg?v=1770652913"},{"product_id":"python-for-data-science-9783031524738","title":"Python for Data Science","description":"\u003cp\u003eThe book is designed to serve as a textbook for courses offered to undergraduate and graduate students enrolled in data science.\u003c\/p\u003e\n\u003cp\u003eThis book aims to help the readers understand the basic and advanced concepts for developing simple programs and the fundamentals required for building machine learning models. The book covers basic concepts like data types, operators, and statements that enable the reader to solve simple problems. As functions are the core of any programming, a detailed illustration of defining \u0026amp; invoking functions and recursive functions is covered.\u003c\/p\u003e\n\u003cp\u003eBuilt-in data structures of Python, such as strings, lists, tuples, sets, and dictionary structures, are discussed in detail with examples and exercise problems. Files are an integrated part of programming when dealing with large data. File handling operations are illustrated with examples and a case study at the end of the chapter.\u003c\/p\u003e\n\u003cp\u003eWidely used Python packages for data science, such as Pandas, Data Visualization libraries, and regular expressions, are discussed with examples and case studies at the end of the chapters. 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By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape.\n\nWhat you will learn\n● Dissect key factors influencing model interpretability and its different types.\n● Apply post-hoc and inherent techniques to enhance AI transparency.\n● Build explainable AI (XAI) solutions using Python frameworks for different models.\n● Implement explainability methods for deep learning at global and local levels.\n● Explore cutting-edge research on transparency in transformers and LLMs.\n● Learn the role of XAI in Responsible AI, including key tools and methods.\n\nTable of Contents\n1. Interpreting Interpretable Machine Learning\n2. Model Types and Interpretability Techniques\n3. Interpretability Taxonomy and Techniques\n4. Feature Effects Analysis with Plots\n5. Post-Hoc Methods\n6. Anchors and Counterfactuals\n7. Interpretability in Neural Networks\n8. Explainable Neural Networks\n9. Explainability in Transformers and Large Language Models\n10. Explainability and Responsible AI\nIndex\n\nAbout the Authors\nAruna Chakkirala a seasoned technical leader and currently serves as an AI Solutions Architect at Microsoft. She was instrumental in the early adoption of Generative AI and constantly strives to keep pace with the evolving domain. As a Data Scientist, she has built Supervised and Unsupervised models to address cybersecurity problems. She holds a patent for her pioneering work in community detection for DNS querying. Her technical expertise spans multiple domains, including Networks, Security, Cloud, Big Data, and AI. She believes that the success of real-world AI applications increasingly depends on well- defined architectures across all encompassing domains. 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By outlining actionable strategies and best practices, it supports readers in responsibly integrating AI into their workflows while maintaining ethical standards. 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The book examines the fundamentals of AI for cybersecurity as a multidisciplinary subject, describes how to design, build, and operate AI technologies and strategies to achieve a more secure cyberspace, and provides why-what-how of each AI technique-cybersecurity task pair to enable researchers and practitioners to make contributions to the field of AI for cybersecurity.  \u003c\/p\u003e\u003cp\u003eThis book is aligned with the National Science and Technology Council’s (NSTC) 2023 Federal Cybersecurity Research and Development Strategic Plan (RDSP) and President Biden’s Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Learning objectives and 200 illustrations are included throughout the text.  \u003c\/p\u003e\u003cp\u003eWritten by a team of highly qualified experts in the field, \u003ci\u003eAI for Cybersecurity\u003c\/i\u003e discusses topics including:  \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eRobustness and risks of the methods covered, including adversarial ML threats in model training, deployment, and reuse\u003c\/li\u003e\n\u003cli\u003ePrivacy risks including model inversion, membership inference, attribute inference, re-identification, and deanonymization\u003c\/li\u003e\n\u003cli\u003eForensic and formal methods for analyzing, auditing, and verifying security- and privacy-related aspects of AI components\u003c\/li\u003e\n\u003cli\u003eUse of generative AI systems for improving security and the risks of generative AI systems to security\u003c\/li\u003e\n\u003cli\u003eTransparency and interpretability\/explainability of models and algorithms and associated issues of fairness and bias\u003c\/li\u003e\n\u003c\/ul\u003e  \u003cp\u003e\u003ci\u003eAI for Cybersecurity \u003c\/i\u003eis an excellent reference for practitioners in AI for cybersecurity related industries such as commerce, education, energy, financial services, healthcare, manufacturing, and defense. 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Examines the integration of AI in drones and UAS, enhancing operational efficiency and investigative processes Covers the evolving landscape of drone technology, including swarms, autonomous systems, and forensic methodologies Focuses on specific sectors such as agriculture, logistics, and disaster response, offering practical insights, real-life examples, and future scenarios Explores the role of drones in delivery and logistics, transforming transportation systems by improving efficiency and reducing costs Explains the use of drones for public safety, smart city disaster response, and forensic investigations, providing critical insights for emergency operations This reference book serves as a comprehensive guide for professionals, researchers, and scholars interested in the rapidly advancing world of drone technology.","brand":"ThinkEDU","offers":[{"title":"Perpetual","offer_id":50610327388446,"sku":"9781040394939","price":225.0,"currency_code":"USD","in_stock":true},{"title":"365 Day Option","offer_id":50610327421214,"sku":"9781040394939R365","price":146.25,"currency_code":"USD","in_stock":true},{"title":"180 Day Option","offer_id":50610327453982,"sku":"9781040394939R180","price":123.75,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0800\/4435\/9966\/files\/1024_3a187441-2aee-4fdf-972b-5088d61698f4.jpg?v=1770988922"},{"product_id":"understanding-ai-in-cybersecurity-and-secure-ai-9783031915246","title":"Understanding AI in Cybersecurity and Secure AI","description":"This book presents an overview of the emerging topics in Artificial Intelligence (AI) and cybersecurity and addresses the latest AI models that could be potentially applied to a range of cybersecurity areas. 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The authors discuss state-of-the-art Internet of Things (IoT) security and describe various challenges of AI for cybersecurity, such as data diversity, model customising, explainability, and time complexity and includes some future work. They provide a comprehensive understanding of adversarial machine learning including the up-to-date adversarial attacks and defences. The book finishes off with a discussion of the challenges and future work in secure AI. 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