talk-data.com

After the huge success of the Agentic AI Conference this past May with 51,000+ attendees, we’re back for the second edition - happening September 15–19, 2025 - and it’s fully virtual!

Join us for five days of expert-led sessions and hands-on workshops from the builders shaping the future of agentic systems:

✅ Designing Intelligent Agents: The role of memory, cognition, and planning ✅ Architecting Scalable Multi-Agent Workflows: From coordination to orchestration ✅ Managing Security and Governance in MCP Deployment: Best practices for safe and compliant AI ✅ Building Agentic Research Assistants with Reka: Multi-source intelligence for business and markets ✅ Giving Eyes to Your AI: Building Vision-Enabled Agents with Haystack ✅ From Data to Agents: Building GraphRAG systems with structured & unstructured data ✅ Going Beyond the Chatbot: Building event-driven agents with GitHub Webhooks ✅ Workshop on Visualizing Transformer Models by Luis Serrano ✅ Workshop on Building AI Agents with Vector Databases by Weaviate ✅ Workshop on Building Agentic AI for Semantic Search by Pinecone ✅ Workshop on Building Smarter Agents, Faster by Arize ✅ Workshop on LandingAI’s Agentic Document Extraction

Registration is required — Register Now to secure your spot!

After the huge success of the Agentic AI Conference this past May with 51,000+ attendees, we’re back for the second edition - happening September 15–19, 2025 - and it’s fully virtual!

Join us for five days of expert-led sessions and hands-on workshops from the builders shaping the future of agentic systems:

✅ Designing Intelligent Agents: The role of memory, cognition, and planning ✅ Architecting Scalable Multi-Agent Workflows: From coordination to orchestration ✅ Managing Security and Governance in MCP Deployment: Best practices for safe and compliant AI ✅ Building Agentic Research Assistants with Reka: Multi-source intelligence for business and markets ✅ Giving Eyes to Your AI: Building Vision-Enabled Agents with Haystack ✅ From Data to Agents: Building GraphRAG systems with structured & unstructured data ✅ Going Beyond the Chatbot: Building event-driven agents with GitHub Webhooks ✅ Workshop on Visualizing Transformer Models by Luis Serrano ✅ Workshop on Building AI Agents with Vector Databases by Weaviate ✅ Workshop on Building Agentic AI for Semantic Search by Pinecone ✅ Workshop on Building Smarter Agents, Faster by Arize ✅ Workshop on LandingAI’s Agentic Document Extraction

Registration is required — Register Now to secure your spot!

After the huge success of the Agentic AI Conference this past May with 51,000+ attendees, we’re back for the second edition - happening September 15–19, 2025 - and it’s fully virtual!

Join us for five days of expert-led sessions and hands-on workshops from the builders shaping the future of agentic systems:

✅ Designing Intelligent Agents: The role of memory, cognition, and planning ✅ Architecting Scalable Multi-Agent Workflows: From coordination to orchestration ✅ Managing Security and Governance in MCP Deployment: Best practices for safe and compliant AI ✅ Building Agentic Research Assistants with Reka: Multi-source intelligence for business and markets ✅ Giving Eyes to Your AI: Building Vision-Enabled Agents with Haystack ✅ From Data to Agents: Building GraphRAG systems with structured & unstructured data ✅ Going Beyond the Chatbot: Building event-driven agents with GitHub Webhooks ✅ Workshop on Visualizing Transformer Models by Luis Serrano ✅ Workshop on Building AI Agents with Vector Databases by Weaviate ✅ Workshop on Building Agentic AI for Semantic Search by Pinecone ✅ Workshop on Building Smarter Agents, Faster by Arize ✅ Workshop on LandingAI’s Agentic Document Extraction

Registration is required — Register Now to secure your spot!

In our very first episode, we had the pleasure of chatting with Luis Serrano—one of the top voices in the AI space. Luis Serrano is a technology and science popularizer, researcher, and practitioner and author of the best-selling book Grokking Machine Learning.

He is currently the developer relations lead at Cohere, and has previously worked at several tech companies including Google and Apple. He's also the brains behind popular ML courses on platforms like Coursera and Udacity, and the popular YouTube channel Serrano Academy, with over 135K subscribers.

In this episode, we unpack Luis's fascinating journey, from his childhood and maths fears to a deep-seated passion for it and all things related, including AI and ML. We explore his career path in detail uncovering the pivotal moments and learnings, as he navigated through big tech players, changing gears from Maths to AI and Quantum AI, and how he ultimately found his true calling.

We further venture into the world of AI, exploring its profound impact on education and society—both the positive advancements and the challenges it presents, and how they are reshaping the world and future. And of course, we touch upon the human side of it all—exploring the themes of humanity and empathy and implications for the future.

The podcast ends with a fun and engaging rapid-fire round, again packed with bite-sized learning. So tune in, learn and get inspired!

Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action shows you how to optimize hyperparameter tuning, A/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn’t have to be difficult! You’ll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book’s easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. About the Technology In machine learning, optimization is about achieving the best predictions—shortest delivery routes, perfect price points, most accurate recommendations—in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. About the Book Bayesian Optimization in Action teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you’ll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You’ll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. What's Inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the Reader For machine learning practitioners who are confident in math and statistics. About the Author Quan Nguyen is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. Quotes Using a hands-on approach, clear diagrams, and real-world examples, Quan lifts the veil off the complexities of Bayesian optimization. - From the Foreword by Luis Serrano, Author of Grokking Machine Learning This book teaches Bayesian optimization, starting from its most basic components. You’ll find enough depth to make you comfortable with the tools and methods and enough code to do real work very quickly. - From the Foreword by David Sweet, Author of Experimentation for Engineers Combines modern computational frameworks with visualizations and infographics you won’t find anywhere else. It gives readers the confidence to apply Bayesian optimization to real world problems! - Ravin Kumar, Google

Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. About the Technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the Book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's Inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the Reader For readers who know basic Python. No machine learning knowledge necessary. About the Author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Quotes Did you think machine learning is complicated and hard to master? It’s not! Read this book! Serrano demystifies some of the best-held secrets of the machine learning society. - Sebastian Thrun, Founder, Udacity The first step to take on your machine learning journey. - Millad Dagdoni, Norwegian Labour and Welfare Administration A nicely written guided introduction, especially for those who want to code but feel shaky in their mathematics. - Erik D. Sapper, California Polytechnic State University The most approachable introduction to machine learning I’ve had the pleasure to read in recent years. Highly recommended. - Kay Engelhardt, devstats