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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🔅 AI Workshop: Advanced Chatbot Development

🌐 Author: Axel Sirota
🔰 Level: Advanced

Duration: 3h 38m

🌀 This course equips intermediate data scientists and ML engineers with the practical skills to design, optimize, and deploy advanced chatbots that enhance customer experiences.


📗 Topics: Large Language Models, Generative AI, Chatbot Development

📤 Join Artificial intelligence for more courses
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👨‍💻 Gamma — Create presentations in a few clicks with AI

🛠 A recent update to the AI ​​service for creating presentations Gamma has expanded its capabilities: now it generates not only text and images, but also tables with graphs, turns slides into cards for social networks, and pictures can not only be generated by neural networks, but also selected from the author's illustrations.

⚙️ How to create a presentation in Gamma?

🔹 Visit the Gamma website, Click "Start for free" and register.

🔹 Click “Create a new AI” and select one of the presentation content options: based on your notes, generate an AI entirely, or upload a finished presentation for editing.

🔹 Select the project type (presentation, website, document or social media post), number of slides, language and click "Create summary".

🔹 Check the contents of the outline. Choose the design, the method of creating images, enter your style preferences and click "Generate!"

🔗 Links: https://gamma.app
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😎 We found a great neural network for voice-over texts for you

🔰Hailuo.ai — AI that will read text with any voice

🔰Completely clones voice in just 10 seconds, has a library of 300+ voices in different languages ​​and with different intonations


💥 And also the neural network is absolutely free and there is no censorship!

🔗 Links: https://www.minimax.io/audio
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🔗 Vanna AI

🛠 Vanna is an MIT-licensed open-source Python RAG (Retrieval-Augmented Generation) framework for SQL generation and related functionality.


🤖Chat with your SQL database 📊.
🔰Accurate Text-to-SQL Generation via LLMs using RAG 🔄.

🔗 Links: https://github.com/vanna-ai/vanna
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🔗 RAG Developer Stack
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🔅 LLaMa for Developers

🌐 Author: Denys Linkov
🔰 Level: Intermediate

Duration: 1h 49m

🌀 Get an introduction to the architecture, process of fine tuning, deploying, and prompting in the popular open source LLaMa model.


📗 Topics: AI Software Development, LLaMA, Large Language Models

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📂 Full denoscription

In this course, learn how to customize open-source AI models with one of the most common open-source models, LLaMa (Large Language Model Meta AI). Instructor Denys Linkov shares a hands-on approach to working with LLaMa, explaining LLaMa architecture, prompting, deploying, and training models. He uses a series of Python notebooks to show you how to adapt LLaMa to your use cases and employ it in an enterprise or startup environment.
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If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns.

Let's break it down.

🔹 Reflection
You give the agent an input.
The agent then "reflects" on its output, and based on feedback, improves and refines.

Ideal tools to use:
- Base model (e.g. GPT-4o)
- Fine-tuned model (to give feedback)
- n8n to set up the agent.

🔹 RAG-based
You give the agent a task.
The agent has the ability to query an external knowledge base to retrieve specific information needed.

Ideal tools to use:
- Vector Database (e.g. Pinecone).
- UI-based RAG (Aidbase is the #1 tool).
- API-based RAG (SourceSync is a new player on the market, highly promising).

🔹 AI Workflow
This is a "traditional" automation workflow that uses AI to carry out subtasks as part of the flow.

Ideal tools to use:
- n8n to handle the workflow.
- GPT-4o, Claude, or other models that can be accessed through API (basic HTTP requests).

If you can master these 3 patterns well, you can solve a very broad range of different problems.
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🔗 AI vs. ML

AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.


🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed.

AI thinks, ML learns. Simple as that!
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🔗 AI vs. ML

AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.


🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed.

AI thinks, ML learns. Simple as that!
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