🔗 AI Agents for Beginners - A Course

This course has 10 lessons covering the fundamentals of building AI Agents.

🔰Each lesson covers its own topic so start wherever you like!

🔰There is multi-language support for this course.

🔗 Links:
https://github.com/microsoft/ai-agents-for-beginners

🔗 ML Libraries

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🔅 Hands-On PyTorch Machine Learning

🌐 Author: Helen Sun
🔰 Level: Intermediate

⏰ Duration: 56m

🌀 Discover the fundamentals of creating machine learning models with PyTorch, the open-source machine learning framework.

📗 Topics: PyTorch, Machine Learning

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📂 Full denoscription

Many of the worlds most exciting and innovative new tech projects leverage the power of machine learning. But if you want to set yourself apart as a data scientist or machine learning engineer, you need to stay up to date with the current tools and best practices for creating effective, predictable models.In this course, instructor Helen Sun shows you how to get up and running with PyTorch, the open-source machine learning framework known for its simplicity, performance, and APIs. Explore the basic concepts of PyTorch, including tensors, operators, and conversion to and from NumPy, as well as how to utilize autograd, which tracks the history of every computation recorded by the framework. By the end of this course, youll also be equipped with a new set of skills to get the most out of Torchvision, Torchaudio, and Torchtext.

📱Artificial intelligence
📱Hands-On PyTorch Machine Learning

🔗 Instill Core is a universal tool for working with unstructured data.

This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines.

Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation.

🔗 GitHub

🔗 AI Engineer Roadmap

🔗 What is an AI Agent?

An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.

Key characteristics of AI agents are as follows:

An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control.

- Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions.

- Agents must be able to perceive and process the information available from their environment.

- Agents can also use tools such as accessing the internet, using code interpreters and making API calls.

- Agents can also collaborate with other agents or humans.

Multiple types of AI agents are available such as learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.

A system with AI agents can be built with different architectural approaches.

1 - Single Agent: Agents can serve as personal assistants.
2 - Multi-Agent: Agents can interact with each other in collaborative or competitive ways.
3 - Human Machine: Agents can interact with humans to execute tasks more efficiently.

🔗 Machine learning Project ideas

🔅 Design to Code: Using AI to Build Faster

🌐 Author: Drew Falkman
🔰 Level: Intermediate

⏰ Duration: 1h 21m

🌀 Learn about the artificial intelligence tools that can improve and speed up your design process.

📗 Topics: Artificial Intelligence for Design, Software Development, Artificial Intelligence

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