AI and Machine Learning – Telegram
AI and Machine Learning
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Learn Data Science, Data Analysis, Machine Learning, Artificial Intelligence, and Python with Tensorflow, Pandas & more!
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📂 Full denoscription

In this comprehensive course, AI and LLM expert Sinan Ozdemir shares with you the knowledge and skills to assess LLM performance effectively. Get a detailed introduction to the process of evaluating LLMs, Multimodal AI, and AI-powered applications like agents and RAG. Learn how to thoroughly assess and evaluate these powerful and often unwieldy AI tools so you can make sure they meet your real-world needs. This course prepares you to evaluate and optimize LLMs so you can produce cutting edge AI applications.
This course was created by Pearson. We are pleased to host this training in our library.
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0. Introduction.zip
8.9 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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1. Foundations of LLM Evaluation.zip
104.7 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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2. Evaluating Generative Tasks.zip
220 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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3. Evaluating Understanding Tasks.zip
137.4 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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4. Using Benchmarks Effectively.zip
156.8 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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5. Probing LLMs for a World Model.zip
137.9 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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6. Evaluating LLM Fine-Tuning.zip
212.1 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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7. Case Studies.zip
257.4 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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8. Summary of Evaluation and Looking Ahead.zip
47.8 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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9. Conclusion.zip
4.1 MB
📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)
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TOP ML Interview Problems
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⚡️ 200+ ready-made noscripts for n8n

Found a simple and useful resource: a GitHub repository with 200+ free workflows for n8n.

Topics: sales, marketing, financial accounting, coding, and personal productivity.

What is n8n
- Open-source no-code automation tool
- Visual builder: connect blocks to create a process
- Hundreds of integrations: email, CRM, spreadsheets, messengers, webhooks
- You can add your own logic in JavaScript
- Run on schedule or event, works in the cloud or on your own server

How to use:
1) Download the desired workflow (.json) and import it into n8n
2) Insert your API keys and credentials into the blocks
3) Check the steps and enable running by cron or webhook

▪️ Github

Update - another 300 ready solutions: https://github.com/kossakovsky/n8n-installer
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Mastering LLMs is a journey, and our infographic gives you a sneak peek into the key steps to success. From fundamentals to deployment, it’s all about having the right roadmap.
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What is RAG? 🤖📚

RAG stands for Retrieval-Augmented Generation.
It’s a technique where an AI model first retrieves relevant info (like from documents or a database), and then generates an answer using that info.

🧠 Think of it like this:
Instead of relying only on what it "knows", the model looks things up first - just like you would Google something before replying.

🔍 Retrieval + 📝 Generation = Smarter, up-to-date answers!
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🔅 Fine-Tuning for LLMs: from Beginner to Advanced

🌐 Author: Axel Sirota
🔰 Level: Advanced

Duration: 3h 25m

🌀 Gain the expertise you need in Large Language Models (LLMs), a rapidly evolving field in AI, including hands-on practice.


📗 Topics: Large Language Models, Generative AI, Fine Tuning

📤 Join Artificial intelligence for more courses
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