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If you're working with LLMs or care about building production-grade applications using LLMs, this course is a must. It features contributions from industry leaders such as Hamel H., Jeremy Howard, Sophia Yang, Ph.D., Simon Willison, and JJ Allaire, who bring expertise from companies like Fast.ai, Anaconda, and others. Covering diverse topics like Retrieval-Augmented Generation (RAG), fine-tuning, evaluation methods, and prompt engineering, the course offers practical insights and best practices, making it distinctively practical and comprehensive.

Plan Like a Graph (PLaG), a prompting technique by researchers at the University of Oxford and other institutes, instructs LLMs to decompose the task like a graph of subtasks with timing and dependencies. PLaG is especially efficient for asynchronous tasks, that require parallel and sequential execution of steps. But it is not without limits.

Quick summarization on how LLMs are trained.

New pre-print alert!! https://lnkd.in/dkMRdsmr Main takeaway: Takacs-Fiksel estimation (TF) which is the state-of-the-art for parameter estimation for Gibbs point processes (if you include its special cases: psuedolikelihood, logistic regression, etc.) is a limiting case of Point Process Learning (PPL) which is a newly developed method (see https://lnkd.in/d_FuJ488). We also show that PPL outperforms TF in a simulation study for some common Gibbs models. For those interested, here is the story of the work behind this pre-print: This Wednesday I posted my paper titled "Comparison of Point Process Learning and its special case Takacs-Fiksel estimation" on arXiv. It has been a long road since I started my PhD in August 2022. Most of the time I have been swamped with courses, teaching, travel and other duties that come with academic life. Research-wise I started in the spring of 2023 with a simulation study comparing PPL with pseudolikelihood for Gibbs models. That was the idea for my first paper, and I presented my results at several conferences. In February 2024 we submitted a conference paper about my findings to the conference https://lnkd.in/dn6HhXsm, which is to appear during the summer. During the fall of 2023 I worked on exploring statistical properties of PPL. For example, we started to look at TF, of which pseudolikelihood is a special case. At this point it was not clear anymore what should be included in "my first paper". In January we realised that what I have done so far theory-wise and in simulations did not have a clear story, and decided to start with telling one part of the story. This turned into the pre-print I share here today. During the spring of 2024 I spent lots of time proving the main results of the paper and running new simulations and writing, writing, writing. I am so happy to finally share this with all of you! It took longer than expected, but at the same time, it feels so sudden! I am trying now to take a step back and celebrate this milestone before jumping on the next project, but there are lots of fun things to continue working on!

Continuous learning is essential. That’s why I’m excited to share I’ve earned my Transformer Models and BERT Model Badge #GoogleCloudSkillsBoost #GoogleCloudLearning #GoogleCloudBadge

LLMs explained in simple terms 💡

Great post on the basics of LLM’s, and how they work! It provides an excellent explanation of basic conditional probability- which provides the framework and foundation of these models. At the end of the day, it all boils down to probability and statistics. #AI #probability #machinelearning #statistics #largelanguagemodels

If anyone told you that 𝗠𝗟 or 𝗠𝗟𝗢𝗽𝘀 is 𝗲𝗮𝘀𝘆, they were 𝗿𝗶𝗴𝗵𝘁. Here is a simple trick that I learned the hard way ↓ If you are in this domain, you already know that everything changes fast: - a new tool every month - a new model every week - a new project every day You know what I did? I stopped caring about all these changes and switched my attention to the real gold. Which is → "𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀." . Let me explain ↓ When you constantly chase the latest models (aka FOMO), you will only have a shallow understanding of that new information (except if you are a genius or already deep into that niche). But the joke's on you. In reality, most of what you think you need to know, you don't. So you won't use what you learned and forget most of it after 1-2 months. What a waste of time, right? . But... If you master the fundamentals of the topic, you want to learn. For example, for deep learning, you have to know: - how models are built - how they are trained - groundbreaking architectures (Resnet, UNet, Transformers, etc.) - parallel training - deploying a model, etc. ...when in need (e.g., you just moved on to a new project), you can easily pick up the latest research. Thus, after you have laid the foundation, it is straightforward to learn SoTA approaches when needed (if needed). Most importantly, what you learn will stick with you, and you will have the flexibility to jump from one project to another quickly. . I am also guilty. I used to FOMO into all kinds of topics until I was honest with myself and admitted I am no Leonardo Da Vinci. But here is what I did and worked well: - building projects - replicating the implementations of famous papers - teaching the subject I want to learn ... and most importantly, take my time to relax and internalize the information. . To conclude: - learn ahead only the fundamentals - learn the latest trend only when needed What is your learning strategy? Let me know in the comments ↓ #machinelearning #productivity #personaldevelopment . 💡 Follow me for daily content on production ML and MLOps engineering.

A FREE goldmine of tutorials about Prompt Engineering! I’ve just released a brand-new GitHub repo as part of my Gen AI educative initiative.  You'll find anything prompt-engineering-related in this repository. From simple explanations to the more advanced topics. 🔗 Check it out here: https://lnkd.in/dGyJsX4G  The content is organized in the following categories: 1.     Fundamental Concepts 2.     Core Techniques 3.     Advanced Strategies 4.     Advanced Implementations 5.     Optimization and Refinement 6.     Specialized Applications 7.     Advanced Applications As of today, there are 22 individual lessons. ♻️ Repost this if you found it useful. Want to receive more updates about Gen AI tutorials (Prompt Engineering, RAG, Agents) and blog posts on cutting-edge advancements? Check out my newsletter: https://lnkd.in/dS96NkFZ

Bridging Practice and Theory in Prompt Engineering The Prompt Canvas framework represents a critical step in consolidating and simplifying the fragmented knowledge surrounding prompt engineering—a field essential for effectively guiding large language models (LLMs) to achieve desired outcomes. This work complements my own research from Episode 9, where I applied software design patterns to create Prompt Architecture Cards for Engineering (PACE). While the Prompt Canvas focuses on providing a visual, structured framework for learning and applying prompt engineering techniques, the PACE approach emphasizes building reusable, scalable prompt engineering patterns rooted in architectural principles. Together, these two approaches may form a complementary toolkit for advancing both the practice and discipline of prompt engineering. The Prompt Canvas distills methodologies like Chain-of-Thought reasoning, role-based prompting, and iterative optimization into a practical visual guide. Its systematic structure enables practitioners to: • Define task objectives and personas. • Incorporate context, tone, and formatting. • Apply evidence-based techniques such as step-by-step reasoning, placeholder utilization, and rephrasing. While the Prompt Canvas offers a valuable learning resource, my PACE cards extend this by establishing generalized design patterns that operationalize prompts for dynamic, real-world business processes. These frameworks enable practitioners and researchers alike to align AI solutions with strategic goals, fostering scalable, reusable prompt architectures. Here is Episode 9: https://lnkd.in/g579hD5N How do you see structured approaches like this influencing prompt engineering in your work? #Prompt; #gAI; #AI; #LLM; #GPT Marc Rankin; RK Dodani; Christopher Bramwell; Kojo Inkumsah, M.S., PMP, CSM; Dalton Louque; Viji Sripathi; David Knox; Adam Nichols; Wade Broyles; Venu Mantha; Santosh Krishnan; Vincent Patrizio; Leigh-Ann Russell