Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

Video 🎥 🦷 Deep Margin Elevation made predictable Learn how to manage deep proximal margins with a step-by-step DME technique using everX Flow. Elevating subgingival margins to a more accessible level improves isolation, adhesion, and overall restorative outcomes. By using fibre-reinforced composite as a dentine replacement, you can enhance...

15,798 Aufrufe • vor 2 Monaten •via X (Twitter)

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

David Lean explains how one can learn Filmmaking by watching movies and what constitutes a good movie: "Interviewer: Do you believe that the same principles are involved in making a good amateur film as a good professional film? David Lean: Absolutely the same. The only difference lies in the ambitiousness of the production, but a good film is good on any level, amateur or professional. Interviewer: Then you believe that the amateur can learn something about film technique from watching good professional movies? David Lean: Certainly, if he watches them in the proper way. Interviewer: How would you advice an amateur to watch a film in order to learn from it? For example, how would you advise the amateur to watch 'Bridge on the River Kwai' (1957)? David Lean: I would hope that he would be quite incapable of appreciating the technique of the film. I think that really good filmmaking conceals technique. If a film is really successful, the audience should be so caught up in it that they do not notice how it was made. Interviewer: Do you think then that to really study a movie you should see it more than once, the first time for impact and to enjoy it, and the second time to learn from it? David Lean: This sounds like a plug, but I think so. To really study a film you should have reached the point where you are no longer involved in the dramatic action. In a sense you must become bored with the story before you can study the technique. Of course as you become more and more accustomed to watching films in this manner you will automatically become more sensitive to technique on the first viewing. Interviewer: Did you use this technique in learning? David Lean: In my younger days, I used to spend hours in movie houses to watch the cutting. Interviewer: From a Technical stand point what do you think is the mark of a good film? David Lean: It tells its story in pictures. This is always the problem of good filmmaking. This is one of the biggest things that can be learned from any good movie. The amateur who wants to learn from viewing the films should try to constantly notice how the director is telling his story in terms of visuals. 'How can I show it?' is the questing the director must constantly ask himself. Of course, it is faster in shooting and much easier to tell a story in dialogue, but it is not as effective. Dialogue compared to visuals is a bore; you are using a moving picture camera." (David Lean's interview with Charles Reynolds, 1958)

DepressedBergman

91,395 Aufrufe • vor 9 Monaten

New Course: Reinforcement Fine-Tuning LLMs with GRPO! Learn to use reinforcement learning to improve your LLM performance in this short course, built in collaboration with Predibase by Rubrik, and taught by Travis Addair, its Co-Founder and CTO, and Arnav Garg, its Senior Engineer and Machine Learning Lead. Reasoning models have been one of the most important developments in LLMs. Reinforcement Fine-Tuning (RFT) uses rewards to encourage LLMs to find solutions to multi-step reasoning tasks such as solving math problems and debugging code - without needing pre-existing training examples like in traditional supervised fine-tuning. Group Relative Policy Optimization (GRPO) is a reinforcement fine-tuning algorithm gaining rapid adoption. Developed by the DeepSeek team and used to train the R1 reasoning model, GRPO uses reward functions that you can write in Python to assign rewards to model responses. It’s beneficial for tasks with verifiable outcomes and can work well even with fewer than 100 training examples. It can also significantly improve the reasoning ability of smaller LLMs, making applications faster and more cost effective. In this course, you’ll take a technical deep dive into RFT with GRPO. You’ll learn to build reward functions that you can use in the GRPO training process to guide an LLM toward better performance on multi-step reasoning tasks. In detail, you’ll: - Learn when reinforcement fine-tuning is a better fit than supervised fine-tuning, especially for tasks involving multi-step reasoning or limited labeled data. - Understand how GRPO uses programmable reward functions as a more scalable alternative to the human feedback required for other reinforcement learning algorithms, such as RLHF and DPO. - Frame the Wordle game as a reinforcement fine-tuning problem and see how an LLM can learn to plan, analyze feedback, and improve its strategy over time. - Design reward functions that power the reinforcement fine-tuning process. - Learn techniques for evaluating more subjective tasks, such as rating the quality of a text summary, using an LLM as a judge. - Understand why reward hacking happens and how to avoid it by adding penalty functions to discourage undesirable behaviors. - Learn the four key components of the loss calculation in the GRPO algorithm: token probability distribution ratios, advantages, clipping, and KL-divergence. - Launch reinforcement fine-tuning jobs using Predibase’s hosted training services. By the end of this course, you’ll be able to build and fine-tune LLMs using reinforcement learning to improve reasoning without relying on large labeled datasets or subjective human feedback. Please sign up here:

Andrew Ng

86,457 Aufrufe • vor 1 Jahr

Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

Andrew Ng

53,788 Aufrufe • vor 2 Jahren