Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.
"Mastering Automated Machine Learning: Concepts, Tools, and Techniques" is an essential guide for anyone seeking to unlock the full potential of Automated Machine Learning (AutoML), a groundbreaking technology transforming the field of data science. By automating complex and time-consuming processes, AutoML is making machine learning more efficient and accessible to a broader range of professionals. This book offers an in-depth exploration of core principles, state-of-the-art methodologies, and the practical tools that define AutoML. From data preparation and feature engineering to model selection, tuning, and deployment, readers will acquire a thorough understanding of how AutoML streamlines the entire machine learning pipeline. Whether you're a data scientist, machine learning engineer, or software developer eager to harness the power of automation, "Mastering Automated Machine Learning" provides the insights you need to implement cutting-edge AutoML solutions. With practical examples and guidance on using Python-based frameworks, this book equips you to revolutionize your data science projects. Embrace the future of machine learning and optimize your workflows with "Mastering Automated Machine Learning: Concepts, Tools, and Techniques."
Mastering AI-Powered Trading Bots for Options Analyzing Large Amounts of Data Faster Than Humans Can Read AI can help traders make more informed decisions by analyzing large amounts of data and identifying patterns that humans may miss. Some ways AI can help in trading options include: 1. Predictive analytics: AI algorithms can analyze historical market data and predict future price movements, helping traders make more accurate decisions on which options to buy. 2. Sentiment analysis: AI can analyze news articles, social media posts, and other sources of information to gauge market sentiment and identify potential trading opportunities. 3. Risk management: AI can help traders manage risk by analyzing their portfolio and identifying potential risks and opportunities for hedging. 4. Automation: AI can automate the trading process, executing trades based on predetermined criteria and removing human emotion from the decision-making process. 5. Machine learning: AI can continuously learn from past trading data and optimize trading strategies over time, adapting to changing market conditions and improving performance. Overall, AI can help traders make more informed decisions, reduce risk, and potentially increase returns when trading options. Chapter 1: Introduction to AI and Option Trading Welcome to the exciting world of AI-powered trading bots for executing options trades. In this subchapter, we will explore the fundamentals of AI and option trading, providing you with a solid foundation to begin your journey into the world of trading stocks and options. Whether you are a novice trader looking to learn the basics or an experienced investor seeking to leverage the power of AI technology in your trading strategies, this subchapter is designed to help you understand the key concepts and principles that drive success in the world of option trading. First and foremost, it is important to understand what AI is and how it is revolutionizing the way we approach financial markets. Artificial intelligence, or AI, refers to the simulation of human intelligence processes by machines, particularly computer systems. In the context of option trading, AI can be used to analyze vast amounts of data, identify patterns and trends, and make informed decisions about when to buy or sell options. By harnessing the power of AI technology, traders can gain a competitive edge in the market and increase their chances of success.
Chapter 1: Understanding the Concept of AI Prompts 1.1 Introduction to AI Prompts - Definition of AI prompts and their significance in language models - Overview of how AI prompts facilitate human-machine interactions - Examples of popular AI prompt applications 1.2 The Role of Prompts in AI Systems - Explaining the input-output relationship in AI models - Understanding how prompts influence the generated responses - Differentiating between prompts and instructions in AI systems 1.3 Components of an AI Prompt - Breaking down the structure of an AI prompt - Key elements: context, instructions, and formatting - The importance of clarity and specificity in prompts 1.4 Prompt Formats and Styles - Different types of prompt formats: single-turn, multi-turn, etc. - Understanding open-ended and closed-ended prompts - Exploring various prompt styles: conversational, directive, creative, etc. 1.5 Prompt Engineering and Optimization - Strategies for improving prompt quality and effectiveness - Techniques to elicit desired responses from AI models - Experimenting with prompt variations and iterations 1.6 Limitations and Challenges of AI Prompts - Identifying potential pitfalls and limitations of prompt-based interactions - Addressing issues related to bias, fairness, and ethical concerns - Managing ambiguity and handling context-dependent prompts 1.7 Ethical Considerations in AI Prompting - The responsibility of prompt designers in shaping AI behavior - Ensuring fairness, inclusivity, and accountability in prompt design - Guidelines for avoiding harmful or malicious prompts 1.8 Real-world Applications of AI Prompts - Examples of successful AI prompt-driven projects in various industries - Impact of AI prompts in customer support, content generation, decision-making, etc. - Exploring the potential of AI prompts in emerging fields and applications 1.9 Future Directions and Trends in AI Prompts - Current advancements and ongoing research in AI prompting - Predictions for the evolution of AI prompt technologies - Ethical and societal considerations in the future development of AI prompts In this chapter, readers will gain a comprehensive understanding of AI prompts, including their role in AI systems, the components and formats of prompts, techniques for prompt engineering and optimization, limitations and challenges, ethical considerations, real-world applications, and future trends. This foundational knowledge will serve as a stepping stone for readers to delve deeper into the world of AI prompt expertise. Chapter 2: Choosing the Right AI Platform or Model 2.1 Understanding AI Platforms - Overview of different AI platforms available in the market - Cloud-based platforms vs. on-premises solutions - Considerations for selecting the right platform based on scalability, ease of use, and cost-effectiveness 2.2 Types of AI Models - Introduction to different types of AI models: neural networks, deep learning models, transformers, etc. - Exploring pre-trained models vs. custom models - Understanding the strengths and limitations of various AI models for different tasks 2.3 Popular AI Platforms and Models - In-depth review of leading AI platforms (e.g., TensorFlow, PyTorch, OpenAI) and their features - Overview of pre-trained models available on popular platforms - Case studies highlighting successful applications of specific platforms and models 2.4 Evaluating Platform and Model Suitability - Assessing platform compatibility with programming languages and frameworks - Evaluating model performance metrics: accuracy, speed, memory usage, etc. - Considering the availability of necessary tools, libraries, and documentation for development and deployment 2.5 Scalability and Resource Requirements - Understanding the scalability capabilities of AI platforms and models
Are you ready to take your AI training skills to the next level? In "Mastering AI Model Training" by Kris Hermans, you'll embark on a transformative journey that will empower you to train highly accurate and efficient artificial intelligence models. Uncover Advanced Techniques and Best Practices As AI continues to revolutionize industries, the ability to train powerful and optimized models is paramount. In this comprehensive guide, Kris Hermans reveals the secrets to mastering AI model training. Explore advanced techniques, cutting-edge algorithms, and industry best practices that will propel your AI training expertise to new heights. Become an Expert in Training AI Models Whether you're a seasoned data scientist or a passionate AI enthusiast, this book provides a structured approach to mastering AI model training. Kris Hermans demystifies complex concepts and presents them in a clear and practical manner. Through real-world examples and hands-on exercises, you'll develop the skills and intuition necessary to train AI models that achieve exceptional performance. From Fundamentals to Advanced Topics "Mastering AI Model Training" covers the full spectrum of AI training, starting from the basics of data preprocessing and feature engineering and progressing to advanced topics such as transfer learning, hyperparameter optimization, and model compression. Gain a deep understanding of different training algorithms and architectures, and learn how to adapt them to various domains and use cases. Optimize Training for Performance and Efficiency Discover strategies for improving model performance, reducing training time, and optimizing resource utilization. Explore techniques for handling large datasets, distributed training, and leveraging hardware accelerators such as GPUs and TPUs. With Kris Hermans as your guide, you'll learn how to train models that deliver superior results while maximizing computational efficiency. Practical Applications and Real-World Case Studies Immerse yourself in practical applications of AI model training across industries such as healthcare, finance, manufacturing, and more. Dive into captivating case studies that demonstrate how AI training is transforming businesses and driving innovation. Gain insights into the challenges faced by organizations and learn how they leverage AI training techniques to gain a competitive edge. Ethical Considerations and Responsible AI With great power comes great responsibility. "Mastering AI Model Training" addresses the ethical considerations associated with AI training and highlights the importance of responsible AI practices. Learn how to mitigate biases, ensure fairness, and navigate ethical challenges to build AI models that are not only accurate and efficient but also ethical and trustworthy.
TAGLINE Elevate your Unreal Engine skills with This Essential Guide to AI-Driven Game Development! KEY FEATURES ● Comprehensive coverage of AI techniques and their Unreal Engine implementation ● Practical hands-on exercises and real-world examples for seamless AI integration ● Covers all important AI workflows in Unreal Engine DESCRIPTION In the rapidly evolving world of game development, integrating Artificial Intelligence (AI) into your Unreal Engine projects is no longer optional—it’s essential. Mastering AI for Unreal Engine is your definitive guide to unlocking the transformative power of AI, propelling your games beyond the ordinary and into the realm of cutting-edge, immersive experiences. Through a step-by-step approach, you will explore a wide range of AI techniques, including pathfinding, behavior trees, perception systems, and the powerful Environment Query System (EQS). Each chapter delves into the theoretical underpinnings of these AI components, followed by hands-on exercises and real-world examples that demonstrate their integration within Unreal Engine. We will learn to create intelligent, dynamic, and engaging game experiences by leveraging the power of AI. What sets this book apart is its deep dive into emerging AI frameworks like the Mass Framework, giving you a strategic edge in adopting the latest trends and technologies that are shaping the future of gaming. You’ll learn how to optimize your AI systems, enhance performance, and master debugging techniques critical for smooth, efficient development processes. Mastering AI for Unreal Engine doesn’t just teach you AI—it empowers you to innovate, create intelligent game worlds, and stand out in the fiercely competitive game development landscape. WHAT WILL YOU LEARN ● Set up and navigate Unreal Engine to establish a strong foundation for AI development. ● Learn Blueprints, create class Blueprints, and use Blueprint communication for interactive AI components. ● Build AI characters, and master navigation meshes and pathfinding for dynamic movement. ● Create complex AI behaviors using Behavior Trees and Blackboard components. ● Implement AI Perception to add sight and hearing, making your AI characters more aware and reactive. ● Use State Trees to manage AI states with evaluators, tasks, and transitions. ● Leverage the Environmental Query System (EQS) to enable AI to make context-aware decisions. ● Debug and optimize AI with Unreal’s visual logging and debugging tools. ● Explore advanced AI design techniques like Data Oriented Design, Mass AI, and ZoneGraph. WHO IS THIS BOOK FOR? This book is tailored for game developers, aspiring designers, and anyone interested in integrating AI into Unreal Engine projects. Suitable for beginners to experienced professionals, it’s ideal for students, hobbyists, and industry pros looking to enhance their AI skills. Whether you're creating Non-Player Characters (NPCs), designing complex decision-making systems, or advancing game AI, this book provides the tools and knowledge you need to succeed. TABLE OF CONTENTS 1. Introduction to Unreal Engine 2. Unreal Blueprints 3. Understanding AI in Games 4. Navigation and Pathfinding 5. Behavior Trees and Blackboards 6. AI Perception 7. StateTree 8. Environment Query System 9. SmartObjects 10. Debugging AI in Unreal Engine 11. Mass Framework and Future Trends in GameAI Index
Creating Transparent AI From agriculture to transportation, entertainment to medicine, and banking to social media, artificial intelligence (AI) is changing how humans do practically everything. We experience AI in our daily lives through our fitness trackers, home digital assistant systems, and curated news services, to name a few examples. For talent development, this is no different. The fields of artificial intelligence and talent development have been on a collision course for decades, and their convergence has already occurred. It has just taken many in our profession some time to recognize this fact. On the horizon, AI-powered innovations are transforming the workplace and the role of the talent development professional, affecting recruiting to training to compensation. As such, there are actions TD professionals should take now to prepare ourselves and our organizations for the evolving AI revolution. In AI in Talent Development, Margie Meacham describes the benefits, uses, and risks of AI technology and offers practical tools to strengthen and enhance learning and performance programs. In layman’s terms, Meacham demonstrates how we can free time for ourselves by employing a useful robot “assistant,” create a chatbot for specific tasks (such as a new manager bot, a sales coach bot, or new employee onboarding bot), and build personalized coaching tools from AI-processed big data. She concludes each of the six chapters with helpful tips and includes a resource guide with planning tools, templates, and worksheets. Meacham dispels fear of AI’s black box—the term used to describe its unknowability and opacity—and points out ways AI can help us be better at creativity and critical thinking, what we humans do best.
Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.
Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
