How to Fine-tune DeepSeek with Reinforcement Learning [webinar]

Topic 26: What is test-time compute and how to scale it?

We dive into test-time compute and discuss five+ open-source methods for its effective scaling for deep step-by-step models' reasoning.

🌁#86: Four Freedoms of Open AI

– what are they? Defining the future

🦸🏻#9: Does AI Remember? The Role of Memory in Agentic Workflows

we explore SOAR’s legacy, memory types, generative workflows, memory mode in LLM, and AI influence on memory itself

eBook: Mastering AI Agents

Learn how to create powerful, reliable AI agents with Galileo's in-depth eBook

Topic 25: The Keys to Prompt Optimization

Practical Insights for Large Language Models

🌁#85: Curiosity, Open Source, and Timing: The Formula Behind DeepSeek’s Phenomenal Success

How an open-source mindset, relentless curiosity, and strategic calculation are rewriting the rules in AI and challenging Western companies, plus an excellent reading list and curated research collection

🦸🏻#8: Rewriting the Rules of Knowledge: How Modern Agents Learn to Adapt

Exploring the shift from static rules to dynamic reasoning in modern AI agents

Topic 24: What is Cosmos World Foundation Model Platform?

World models are the next big thing that enables Physical AI. Let's explore how NVIDIA makes it happen

🌁#84: Could Program Synthesis Unlock AGI?

we discuss François Chollet's combinatorial approach to reaching AGI, plus offer you a collection of interesting articles, relevant news, and must-read research papers. Dive in!

Topic 23: What is LLM Inference, it's challenges and solutions for it

Plus a Video Interview with the SwiftKV Authors on Reducing LLM Inference Costs by up to 75%

Guest post: Enhancing RAG with RA-DIT: A Fine-Tuning Approach to Minimize LLM Hallucinations*

RA-DIT, or Retrieval-Augmented Dual Instruction Tuning, is a method for fine-tuning both the LLM and the retriever in a RAG setup to enhance overall response quality