Ai Agents In Action Book Pdf Download
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Deep Reinforcement Learning in Action
Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap
Lecture Notes | Organisational Behavior Book PDF (BBA/MBA Management eBook Download)
The Book Organisational Behavior Notes PDF Download (BBA/MBA Management Textbook 2023-24): Lecture Notes with Revision Guide (Organisational Behavior Textbook PDF: Notes, Definitions & Explanations) covers revision notes from class notes & textbooks. Organisational Behavior Lecture Notes PDF covers chapters' short notes with concepts, definitions and explanations for BBA, MBA exams. Organisational Behavior Notes Book PDF provides a general course review for subjective exam, job's interview, and test preparation. The eBook Organisational Behavior Lecture Notes PDF to download with abbreviations, terminology, and explanations is a revision guide for students' learning. Organisational behavior definitions PDF download with free eBook's sample covers exam course material terms for distance learning and certification. Organisational Behavior Textbook Notes PDF with explanations covers subjective course terms for college and high school exam's prep. Organisational behavior notes book PDF (MBA/BBA) with glossary terms assists students in tutorials, quizzes, viva and to answer a question in an interview for jobs. Organisational Behavior Study Material PDF to download free book’s sample covers terminology with definition and explanation for quick learning. Organisational Behavior lecture notes PDF with definitions covered in this quick study guide includes: What is Organisational Behavior Notes Foundations of Individual Behavior Notes Attitudes and Job Satisfaction Notes Personality and Values Notes Perception and Individual Decision Making Notes Motivation Concepts Notes Motivation: From Concepts to Applications Notes Emotions and Moods Notes Foundations of Group Behavior Notes Understanding Work Teams Notes Communication Notes Basic Approaches to Leadership Notes Contemporary Issues in Leadership Notes Power and Politics Notes Conflict and Negotiation Notes Foundations of Organization Structure Notes Organizational Culture Notes Human Resource Policies and Practices Notes Organisational Behavior Lecture Notes PDF covers terms, definitions, and explanations: Ability, Accommodating, Action Research, Adjourning Stage, Affect Intensity, Affect, Affective Component, Affective Events Theory (AET), Agreeableness, Anchoring Bias, Anthropology, Appreciative Inquiry (AI), Arbitrator, Assessment Centers, Attitudes, Attribution Theory of Leadership, Attribution Theory, Authentic Leaders, Authority, Automatic Processing, Autonomy, Availability Bias, and Avoiding. Organisational Behavior Complete Notes PDF covers terms, definitions, and explanations: BATNA, Behavioral Component, Behavioral Theories of Leadership, Behaviorally Anchored Rating Scales (BARS), Behaviorism, Big Five Model, Biographical Characteristics, Blog (Web log), Bonus, Boundaryless Organization, Bounded Rationality, Brainstorming, and Bureaucracy. Organisational Behavior Class Notes PDF covers terms, definitions, and explanations: Centralization, Chain of Command, Challenge Stressors, Change Agents, Change, Channel Richness, Charismatic Leadership Theory, Citizenship Behavior, Citizenship, Coercive Power, Cognitive Component, Cognitive Dissonance, Cognitive Evaluation Theory, Cohesiveness, Collaborating, Collectivism, Communication Apprehension, Communication Process, Communication, Competing, Compromising, Conceptual Skills, Conciliator, Confirmation Bias, Conflict Management, Conflict Process, Conflict, and Conformity. Organisational Behavior Notes Book PDF covers terms, definitions, and explanations: Organic Model, Organization, Organisational Behavior (OB), Organizational Climate, Organizational Commitment, Organizational Culture, Organizational Demography, Organizational Development (OD), Organizational Justice, Organizational Structure, Organizational Survival, Organizing, and Outcomes. And many more terms and abbreviations!
AI Agents in Action
Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs. From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks. Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: • Understand and implement AI agent behavior patterns • Design and deploy production-ready intelligent agents • Leverage the OpenAI Assistants API and complementary tools • Implement robust knowledge management and memory systems • Create self-improving agents with feedback loops • Orchestrate collaborative multi-agent systems • Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving. About the technology Most production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems. About the book In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI. What's inside • Knowledge management and memory systems • Feedback loops for continuous agent learning • Collaborative multi-agent systems • Speech and computer vision About the reader For intermediate Python programmers. About the author Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning. Table of Contents 1 Introduction to agents and their world 2 Harnessing the power of large language models 3 Engaging GPT assistants 4 Exploring multi-agent systems 5 Empowering agents with actions 6 Building autonomous assistants 7 Assembling and using an agent platform 8 Understanding agent memory and knowledge 9 Mastering agent prompts with prompt flow 10 Agent reasoning and evaluation 11 Agent planning and feedback A Accessing OpenAI large language models B Python development environment