📱Artificial intelligence
📱Introduction to Artificial Intelligence

This repository contains a collection of everything needed to work with libraries related to AI and LLM.

More than 120 libraries, sorted by stages of LLM development:

→ Training, fine-tuning, and evaluation of LLM models
→ Integration and deployment of applications with LLM and RAG
→ Fast and scalable model launching
→ Working with data: extraction, structuring, and synthetic generation
→ Creating autonomous agents based on LLM
→ Prompt optimization and ensuring safe use in production

🔗 Link: https://github.com/Shubhamsaboo/awesome-llm-apps

🔗 How to use Machine Learning to predict fraud

1. Identify project objectives

Determine the key business objectives upon which the machine learning model will be built.
For instance, your goal may be like:

- Reduce false alerts
- Minimize estimated chargeback ratio
- Keep operating costs at a controlled level

2. Data preparation

To create fraudster profiles, machines need to study about previous fraudulent events from historical data. The more the data provided, the better the results of analyzation. The raw data garnered by the company must be cleaned and provided in a machine-understandable format.

3. Constructing a machine learning model

The machine learning model is the final product of the entire ML process.
Once the model receives data related to a new transaction, the model will deliver an output, highlighting whether the transaction is a fraud attempt or not.

4. Data scoring

Deploy the ML model and integrate it with the company’s infrastructure.

For instance, whenever a customer purchases a product from an e-store, the respective data transaction will be sent to the machine learning model. The model will then analyze the data to generate a recommendation, depending on which the e-store’s transaction system will make its decision, i.e., approve or block or mark the transaction for a manual review. This process is known as data scoring.

5. Upgrading the model

Just like how humans learn from their mistakes and experience, machine learning models should be tweaked regularly with the updated information, so that the models become increasingly sophisticated and detect fraud activities more accurately.

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🛠 Smithery AI – An AI platform for automating everyday tasks, compatible with various services.

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🔗 Links: https://smithery.ai

🔅 Building AI Applications with Amazon Bedrock

🌐 Author: Noah Gift
🔰 Level: Intermediate

⏰ Duration: 1h 7m

🌀 Learn how to build real-world AI applications using Amazon Bedrock.

📗 Topics: Amazon Bedrock, Artificial Intelligence, Application Development

📤 Join Artificial intelligence for more courses

📱Artificial intelligence
📱Building AI Applications with Amazon Bedrock

🔗 Harbor — a local stack for working with LLM in one click.

This tool simplifies launching local language models and related services — from web interfaces to RAG and voice interaction. Everything runs in Docker and is configured with a couple of commands.

Harbor automatically integrates components, for example, SearXNG is immediately connected to Open WebUI for web search, and ComfyUI — for image generation. Suitable for those who want to quickly deploy a local environment for AI experiments.

🔗 GitHub

💥MassGen

🛠MassGen is a system for interaction between different AI agents, using collaborative artificial intelligence to solve complex tasks by distributing assignments among multiple agents.

🔰Key features include model synergy, parallel processing, knowledge sharing, consensus building, and live visualization, enabling agents to work effectively together and achieve the best results.

🔰The complex architecture of MassGen supports integration with three major model providers: Google Gemini, OpenAI, and xAI Grok, and also offers the ability to extend functionality with custom tools and an interactive mode for conducting dialogues.

🔗 Links: https://github.com/Leezekun/MassGen

🔅 Complete Guide to Evaluating Large Language Models (LLMs)

🌐 Author: Sinan Ozdemir
🔰 Level: Intermediate

⏰ Duration: 7h 56m

🌀 Equip yourself with the knowledge and skills to assess LLM performance effectively.

📗 Topics: Retrieval-Augmented Generation, Large Language Models, Artificial Intelligence

📤 Join Artificial intelligence for more courses

📂 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.

📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)

📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)

📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)

📱Artificial intelligence
📱Complete Guide to Evaluating Large Language Models (LLMs)