Download or read book Large Language Models in Finance A Deep Dive written by Anand Vemula and published by Anand Vemula. This book was released on with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Large Language Models in Finance: A Deep Dive" offers an insightful exploration into the application of advanced language models within the finance sector. This book delves into the transformative impact of large language models (LLMs) on various aspects of finance, offering a comprehensive overview suitable for both novices and experts in the field. Through the lens of LLMs, readers gain a deeper understanding of how natural language processing (NLP) techniques are revolutionizing financial operations. The book begins by elucidating the significance of LLMs in finance, highlighting their role in tasks such as sentiment analysis, financial forecasting, risk management, and fraud detection. With a focus on practical applications, "Large Language Models in Finance" provides insights into how LLMs are utilized for sentiment analysis, enabling financial professionals to gauge market sentiment and make informed investment decisions. It further explores their role in financial forecasting and predictions, facilitating the development of quantitative trading strategies and enhancing decision-making processes. The book also delves into the crucial aspect of risk management and compliance, showcasing how LLMs aid in identifying potential risks, automating compliance checks, and ensuring adherence to regulatory requirements. Readers gain valuable insights into the ethical considerations surrounding the use of LLMs in finance, including data privacy, bias mitigation, and the responsible deployment of AI technologies. Moreover, "Large Language Models in Finance" offers practical guidance on leveraging LLMs for financial reporting, analysis, and automation, enabling organizations to streamline processes and derive actionable insights from vast amounts of data. The book concludes with a forward-looking perspective, exploring emerging trends, future innovations, and the evolving landscape of LLMs in finance. In summary, "Large Language Models in Finance: A Deep Dive" serves as a comprehensive guide for anyone interested in understanding the transformative potential of LLMs in the finance industry. With its accessible language, practical examples, and forward-thinking insights, this book is essential reading for finance professionals, researchers, and enthusiasts alike.

Download or read book Large Language Models written by Uday Kamath and published by Springer Nature. This book was released on 2024 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.

Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

Download or read book Generative AI and Large Language Models written by Aditya Pratap Bhuyan and published by Aditya Pratap Bhuyan. This book was released on 2024-07-24 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.

Download or read book The Future of Finance with ChatGPT and Power BI written by James Bryant and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhance decision-making, transform your market approach, and find investment opportunities by exploring AI, finance, and data visualization with ChatGPT's analytics and Power BI's visuals Key Features Automate Power BI with ChatGPT for quick and competitive financial insights, giving you a strategic edge Make better data-driven decisions with practical examples of financial analysis and reporting Learn the step-by-step integration of ChatGPT, financial analysis, and Power BI for real-world success Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's rapidly evolving economic landscape, the combination of finance, analytics, and artificial intelligence (AI) heralds a new era of decision-making. Finance and data analytics along with AI can no longer be seen as separate disciplines and professionals have to be comfortable in both in order to be successful. This book combines finance concepts, visualizations through Power BI and the application of AI and ChatGPT to provide a more holistic perspective. After a brief introduction to finance and Power BI, you will begin with Tesla's data-driven financial tactics before moving to John Deere's AgTech strides, all through the lens of AI. Salesforce's adaptation to the AI revolution offers profound insights, while Moderna's navigation through the biotech frontier during the pandemic showcases the agility of AI-focused companies. Learn from Silicon Valley Bank's demise, and prepare for CrowdStrike's defensive maneuvers against cyber threats. With each chapter, you'll gain mastery over new investing ideas, Power BI tools, and integrate ChatGPT into your workflows. This book is an indispensable ally for anyone looking to thrive in the financial sector. By the end of this book, you'll be able to transform your approach to investing and trading by blending AI-driven analysis, data visualization, and real-world applications.What you will learn Dominate investing, trading, and reporting with ChatGPT's game-changing insights Master Power BI for dynamic financial visuals, custom dashboards, and impactful charts Apply AI and ChatGPT for advanced finance analysis and natural language processing (NLP) in news analysis Tap into ChatGPT for powerful market sentiment analysis to seize investment opportunities Unleash your financial analysis potential with data modeling, source connections, and Power BI integration Understand the importance of data security and adopt best practices for using ChatGPT and Power BI Who this book is for This book is for students, academics, data analysts, and AI enthusiasts eager to leverage ChatGPT for financial analysis and forecasting. It's also suitable for investors, traders, financial pros, business owners, and entrepreneurs interested in analyzing financial data using Power BI. To get started with this book, understanding the fundamentals of finance, investment, trading, and data analysis, along with proficiency in tools like Power BI and Microsoft Excel, is necessary. While prior knowledge of AI and ChatGPT is beneficial, it is not a prerequisite.

Download or read book Generative AI and LLMs written by S. Balasubramaniam and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-09-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

Download or read book Machine Learning in Finance written by Matthew F. Dixon and published by Springer Nature. This book was released on 2020-07-01 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Download or read book A Beginner s Guide to Large Language Models written by Enamul Haque and published by Enamul Haque. This book was released on 2024-07-25 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts Step into the revolutionary world of artificial intelligence with "A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts." Whether you're a curious individual or a professional seeking to leverage AI in your field, this book demystifies the complexities of large language models (LLMs) with engaging, easy-to-understand explanations and practical insights. Explore the fascinating journey of AI from its early roots to the cutting-edge advancements that power today's conversational AI systems. Discover how LLMs, like ChatGPT and Google's Gemini, are transforming industries, enhancing productivity, and sparking creativity across the globe. With the guidance of this comprehensive and accessible guide, you'll gain a solid understanding of how LLMs work, their real-world applications, and the ethical considerations they entail. Packed with vivid examples, hands-on exercises, and real-life scenarios, this book will empower you to harness the full potential of LLMs. Learn to generate creative content, translate languages in real-time, summarise complex information, and even develop AI-powered applications—all without needing a technical background. You'll also find valuable insights into the evolving job landscape, equipping you with the knowledge to pursue a successful career in this dynamic field. This guide ensures that AI is not just an abstract concept but a tangible tool you can use to transform your everyday life and work. Dive into the future with confidence and curiosity, and discover the incredible possibilities that large language models offer. Join the AI revolution and unlock the secrets of the technology that's reshaping our world. "A Beginner's Guide to Large Language Models" is your key to understanding and mastering the power of conversational AI. Introduction This introduction sets the stage for understanding the evolution of artificial intelligence (AI) and large language models (LLMs). It highlights the promise of making complex AI concepts accessible to non-technical readers and outlines the unique approach of this book. Chapter 1: Demystifying AI and LLMs: A Journey Through Time This chapter introduces the basics of AI, using simple analogies and real-world examples. It traces the evolution of AI, from rule-based systems to machine learning and deep learning, leading to the emergence of LLMs. Key concepts such as tokens, vocabulary, and embeddings are explained to build a solid foundation for understanding how LLMs process and generate language. Chapter 2: Mastering Large Language Models Delving deeper into the mechanics of LLMs, this chapter covers the transformer architecture, attention mechanisms, and the processes involved in training and fine-tuning LLMs. It includes hands-on exercises with prompts and discusses advanced techniques like chain-of-thought prompting and prompt chaining to optimise LLM performance. Chapter 3: The LLM Toolbox: Unleashing the Power of Language AI This chapter explores the diverse applications of LLMs in text generation, language translation, summarisation, question answering, and code generation. It also introduces multimodal LLMs that handle both text and images, showcasing their impact on various creative and professional fields. Practical examples and real-life scenarios illustrate how these tools can enhance productivity and creativity. Chapter 4: LLMs in the Real World: Transforming Industries Highlighting the transformative impact of LLMs across different industries, this chapter covers their role in healthcare, finance, education, creative industries, and business. It discusses how LLMs are revolutionising tasks such as medical diagnosis, fraud detection, personalised tutoring, and content creation, and explores the future of work in an AI-powered world. Chapter 5: The Dark Side of LLMs: Ethical Concerns and Challenges Addressing the ethical challenges of LLMs, this chapter covers bias and fairness, privacy concerns, misuse of LLMs, security threats, and the transparency of AI decision-making. It also discusses ethical frameworks for responsible AI development and presents diverse perspectives on the risks and benefits of LLMs. Chapter 6: Mastering LLMs: Advanced Techniques and Strategies This chapter focuses on advanced techniques for leveraging LLMs, such as combining transformers with other AI models, fine-tuning open-source LLMs for specific tasks, and building LLM-powered applications. It provides detailed guidance on prompt engineering for various applications and includes a step-by-step guide to creating an AI-powered chatbot. Chapter 7: LLMs and the Future: A Glimpse into Tomorrow Looking ahead, this chapter explores emerging trends and potential breakthroughs in AI and LLM research. It discusses ethical AI development, insights from leading AI experts, and visions of a future where LLMs are integrated into everyday life. The chapter highlights the importance of building responsible AI systems that address societal concerns. Chapter 8: Your LLM Career Roadmap: Navigating the AI Job Landscape Focusing on the growing demand for LLM expertise, this chapter outlines various career paths in the AI field, such as LLM scientists, engineers, and prompt engineers. It provides resources for building the necessary skillsets and discusses the evolving job market, emphasising the importance of continuous learning and adaptability in a rapidly changing industry. Thought-Provoking Questions, Simple Exercises, and Real-Life Scenarios The book concludes with practical exercises and real-life scenarios to help readers apply their knowledge of LLMs. It includes thought-provoking questions to deepen understanding and provides resources and tools for further exploration of LLM applications. Tools to Help with Your Exercises This section lists tools and platforms for engaging with LLM exercises, such as OpenAI's Playground, Google Translate, and various IDEs for coding. Links to these tools are provided to facilitate hands-on learning and experimentation.

Download or read book Speak AI Decoding the Language Revolution written by Mario Capellari and published by Resilientech. This book was released on 2024-02-27 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on a journey into the heart of artificial intelligence (AI) with a guide designed for those intrigued by AI technologies. This book shines a light on the essentials of AI, emphasizing the groundbreaking impact of Large Language Models (LLMs). It offers a clear pathway to understanding AI's evolution, from its foundational concepts like neural networks and natural language processing to the advanced realms of generative AI that shape our future. Discover how AI is revolutionizing various industries, including healthcare, finance, education, and the arts, demonstrating its potential to transform the world. The narrative also tackles the ethical, privacy, and societal challenges accompanying AI's rise, fostering a dialogue on bias, data privacy, and the evolving landscape of work in an AI-enhanced world. Ideal for AI enthusiasts, professionals seeking to grasp AI's relevance to their fields, and anyone interested in the broader implications of AI technologies, this book provides an insightful introduction to AI and LLMs. It invites readers to explore the opportunities and navigate the changes AI brings, preparing them to thrive amidst the ongoing AI revolution.

Download or read book Deep Learning at Scale written by Suneeta Mall and published by "O'Reilly Media, Inc.". This book was released on 2024-06-18 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale

Download or read book Deep Dive into the Power Platform in the Age of Generative AI written by Biswa Pujarini Mohapatra and published by Springer Nature. This book was released on with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Download or read book 50 Algorithms Every Programmer Should Know written by Imran Ahmad and published by Packt Publishing Ltd. This book was released on 2023-09-29 with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Familiarize yourself with advanced deep learning architectures Explore newer topics, such as handling hidden bias in data and algorithm explainability Get to grips with different programming algorithms and choose the right data structures for their optimal implementation Book DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples Create a recommendation engine that suggests relevant movies to subscribers Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs Who this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Download or read book Machine Learning for Finance written by Jannes Klaas and published by . This book was released on 2019-05-30 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plan and build useful machine learning systems for financial services, with full working Python code Key Features Build machine learning systems that will be useful across the financial services industry Discover how machine learning can solve finance industry challenges Gain the machine learning insights and skills fintech companies value most Book Description Machine learning skills are essential for anybody working in financial data analysis. Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will see how to use machine learning to automate manual tasks, identify and address systemic bias, and find new insights and patterns hidden in available data. Machine Learning for Finance encourages and equips you to find new ways to use data to serve an organization's business goals. Broad in scope yet deeply practical in approach, Machine Learning for Finance will help you to apply machine learning in all parts of a financial organization's infrastructure. If you work or plan to work in fintech, and want to gain one of the most valuable skills in the sector today, this book is for you. What you will learn Practical machine learning for the finance sector Build machine learning systems that support the goals of financial organizations Think creatively about problems and how machine learning can solve them Identify and reduce sources of bias from machine learning models Apply machine learning to structured data, natural language, photographs, and written text related to finance Use machine learning to detect fraud, forecast financial trends, analyze customer sentiments, and more Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Who this book is for Machine Learning for Finance is for financial professionals who want to develop and apply machine learning skills, and for students entering the field. You should be comfortable with Python and the basic data science stack, such as NumPy, pandas, and Matplotlib, to get the most out of this book.

Download or read book Mastering ChatGPT written by Mostafa Gamil and published by Mostafa Gamil. This book was released on 2023-01-09 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the full potential of ChatGPT with 'Mastering ChatGPT: Unlocking the Full Potential of Large Language Models'. Written with the help of AI and ChatGPT, this book is the ultimate guide to mastering the capabilities and potential of this powerful technology. Discover the answers to questions like “Will ChatGPT replace employees in Businesses?” ”Will ChatGPT replace Software Professionals?” “Will ChatGPT replace Search Engines?”. With 100+ real chats (Prompts and Responses) with ChatGPT This book is the sequel to 'ChatGPT Basics: An Introduction to the Capabilities and Potential of Large Language Models' and part of the Mastering ChatGPT series, don't miss out on this opportunity to take your understanding of ChatGPT to the next level!

Download or read book Beyond the Algorithm written by Omar Santos and published by Addison-Wesley Professional. This book was released on 2024-01-30 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: As artificial intelligence (AI) becomes more and more woven into our everyday lives—and underpins so much of the infrastructure we rely on—the ethical, security, and privacy implications require a critical approach that draws not simply on the programming and algorithmic foundations of the technology. Bringing together legal studies, philosophy, cybersecurity, and academic literature, Beyond the Algorithm examines these complex issues with a comprehensive, easy-to-understand analysis and overview. The book explores the ethical challenges that professionals—and, increasingly, users—are encountering as AI becomes not just a promise of the future, but a powerful tool of the present. An overview of the history and development of AI, from the earliest pioneers in machine learning to current applications and how it might shape the future Introduction to AI models and implementations, as well as examples of emerging AI trends Examination of vulnerabilities, including insight into potential real-world threats, and best practices for ensuring a safe AI deployment Discussion of how to balance accountability, privacy, and ethics with regulatory and legislative concerns with advancing AI technology A critical perspective on regulatory obligations, and repercussions, of AI with copyright protection, patent rights, and other intellectual property dilemmas An academic resource and guide for the evolving technical and intellectual challenges of AI Leading figures in the field bring to life the ethical issues associated with AI through in-depth analysis and case studies in this comprehensive examination.