AI Builders and Learners SF

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Reinforcement Learning for Finance
Authors: Yves Hilpisch
Published: October 2024
https://learning.oreilly.com/library/view/reinforcement-learning-for/9781098169169/

Chapters:

• 1. Learning Through Interaction
• 2. Deep Q-Learning
• 3. Financial Q-Learning
• II. Data Augmentation
• 4. Simulated Data
• 5. Generated Data
• III. Financial Applications
• 6. Algorithmic Trading
• 7. Dynamic Hedging
• 8. Dynamic Asset Allocation
• 9. Optimal Execution
• 10. Concluding Remarks

Book Description
Reinforcement learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research.
This book is among the first to explore the use of reinforcement learning methods in finance.
Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems.
This book covers:

• Reinforcement learning
• Deep Q-learning
• Python implementations of these algorithms
• How to apply the algorithms to financial problems such as algorithmic trading, dynamic hedging, and dynamic asset allocation

This book is the ideal reference on this topic. You'll read it once, change the examples according to your needs or ideas, and refer to it whenever you work with RL for finance.
Dr. Yves Hilpisch is founder and CEO of The Python Quants, a group that focuses on the use of open source technologies for financial data science, AI, asset management, algorithmic trading, and computational finance.

Learn more about the book here:
https://learning.oreilly.com/library/view/reinforcement-learning-for/9781098169169/

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Building Agentic AI Systems
Authors: Anjanava Biswas, Wrick Talukdar
Published: April 2025

https://learning.oreilly.com/library/view/building-agentic-ai/9781803238753/

or on packt: https://www.packtpub.com/en-us/product/building-agentic-ai-systems-9781801079273

Part 1: Foundations of Generative AI and Agentic Systems
Chapter 1: Fundamentals of Generative AI
Chapter 2: Principles of Agentic Systems
Chapter 3: Essential Components of Intelligent Agents
Part 2: Designing and Implementing Generative AI-Based Agents
Chapter 4: Reflection and Introspection in Agents
Chapter 5: Enabling Tool Use and Planning in Agents
Chapter 6: Exploring the Coordinator, Worker, and Delegator Approach
Chapter 7: Effective Agentic System Design Techniques
Part 3: Trust, Safety, Ethics, and Applications
Chapter 8: Building Trust in Generative AI Systems
Chapter 9: Managing Safety and Ethical Considerations
Chapter 10: Common Use Cases and Applications
Chapter 11: Conclusion and Future Outlook

Book Description
Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems

#### Key Features

• Understand the foundations and advanced techniques of building intelligent, autonomous AI agents
• Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems
• Explore crucial aspects of trust, safety, and ethics in AI agent development and applications
• Purchase of the print or Kindle book includes a free PDF eBook

#### Book Description

Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.
Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.
Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.

#### What you will learn

• Master the core principles of GenAI and agentic systems
• Understand how AI agents operate, reason, and adapt in dynamic environments
• Enable AI agents to analyze their own actions and improvise
• Implement systems where AI agents can leverage external tools and plan complex tasks
• Apply methods to enhance transparency, accountability, and reliability in AI
• Explore real-world implementations of AI agents across industries

#### Who this book is for

This book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.

Learn more about the book here:
https://learning.oreilly.com/library/view/building-agentic-ai/9781803238753/

or on packt: https://www.packtpub.com/en-us/product/building-agentic-ai-systems-9781801079273