AI Builders and Learners SF

​Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group.
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About the book:

• ​Title: Grokking Deep Reinforcement Learning
• ​Authors: Miguel Morales
• ​Published: December 2020

​Manning (Promo code: AIBookClub should give you 45% off: https://www.manning.com/books/grokking-deep-reinforcement-learning
​O'rielly platform: https://learning.oreilly.com/library/view/grokking-deep-reinforcement/9781617295454/
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​Chapters:

• ​1 Introduction to deep reinforcement learning
• ​2 Mathematical foundations of reinforcement learning
• ​3 Balancing immediate and long-term goals
• ​4 Balancing the gathering and use of information
• ​5 Evaluating agents’ behaviors
• ​6 Improving agents’ behaviors
• ​7 Achieving goals more effectively and efficiently
• ​8 Introduction to value-based deep reinforcement learning
• ​9 More stable value-based methods
• ​10 Sample-efficient value-based methods
• ​11 Policy-gradient and actor-critic methods
• ​12 Advanced actor-critic methods
• ​13 Toward artificial general intelligence

​Book Description
​Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You'll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.

​Along the way, you'll see how Union.ai gives your workflows a durable runtime with infrastructure as context, so agents adapt infra resources, recover from failures, and keep moving without re-running completed work. And because it all runs in your own AWS account, you own the full stack end to end: your models, your data, your infrastructure, your IP.

​Then we switch into hack night mode: keep building on the example, bring your own models or datasets, experiment with evals and fine-tuning, extend open models to different parts of your agent, or start something new around the same ideas.

​For teams thinking about sovereign AI, this is a practical starting point for owning more of your stack, the weights, the training loop, the evals, and the infrastructure it runs on, all on AWS.

​Bring a laptop. Leave with a model factory.

​Feel free to join the discussion even if you have not read the book chapters! :)
​Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group.
​-------------------------------------------------
About the book:

• ​Title: Reinforcement Learning from Human Feedback
• ​Authors: Nathan Lambert
• ​Published: August 2026

​Manning (Promo code: AIBookClub should give you 45% off: https://www.manning.com/books/reinforcement-learning-from-human-feedback
​O'rielly platform: https://learning.oreilly.com/library/view/reinforcement-learning-from/9781633434301/
​
​Chapters:

• ​1 Introduction
• ​2 A tiny history of RLHF
• ​3 Training overview
• ​4 Instruction fine-tuning
• ​5 Reward modeling
• ​6 Reinforcement learning
• ​7 Reasoning and inference-time scaling
• ​8 Direct-alignment algorithms
• ​9 Rejection sampling
• ​10 The nature of preferences
• ​11 Preference data
• ​12 Synthetic data
• ​13 Tool use and function calling
• ​14 Over-optimization
• ​15 Regularization
• ​16 Evaluation
• ​17 Crafting model character and products

​Book Description
​Reinforcement Learning from Human Feedback: LLM alignment and post-training helps you understand how modern AI models can be adapted to better match the needs and expectations of their users. Rather than surveying the vast field of reinforcement learning, elite AI researcher Nathan Lambert concentrates exclusively on RLHF and its immediate importance to post-training generative AI models.

This compact book gets right to the point. Early chapters establish the training overview, explain instruction fine-tuning, and build reliable reward models. The middle chapters transition into the heart of alignment, exploring core policy gradient algorithms, Direct Preference Optimization (DPO), and inference-time scaling. Later chapters tackle the messy reality of data, guiding you through preference data collection, synthetic data generation, and the nuances of function calling.

​Feel free to join the discussion even if you have not read the book chapters! :)
​Want to discuss the contents during the reading week? Join the Flyte MLOps Slack group.
​-------------------------------------------------
About the book:

• ​Title: An Illustrated Guide to AI Agents
• ​Authors: Maarten Grootendorst, Jay Alammar
• ​Published: September 2026

​O'rielly platform: https://learning.oreilly.com/library/view/an-illustrated-guide/9798341662681/
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​Chapters:

1. ​Introduction
2. ​Large Language Models
3. ​Reasoning Large Language Models
4. ​Memory
5. ​Tool Usage, Learning, and Protocols
6. ​Planning and Reflection
7. ​Evaluating Agents
8. ​8. Multi-Agent Systems
9. ​Multi-Modal Understanding
10. ​Code Agents and Code LLMs

​Book Description
​Artificial intelligence is entering a new phase. AI agents can now reason, plan, and act with increasing independence. From accelerating scientific breakthroughs to supporting creative work, these systems are quickly reshaping industries and everyday life. This book provides the conceptual foundation and practical insights you need to understand—and effectively work with—this emerging technology.

​Through hundreds of clear graphic illustrations, Maarten Grootendorst and Jay Alammar explain how AI agents are built, how they think, and where they're heading. Designed for professionals, students, and curious learners alike, this guide goes beyond the buzz to reveal what's actually happening inside these systems, why it matters, and how to apply the knowledge in real-world contexts. An Illustrated Guide to AI Agents is your essential reference for navigating the next frontier of artificial intelligence.