Generative AI, Machine Learning

Useful links related from Generative AI, ML space

Collections

• Gen AI- Collection of Articles on AI Code Generation and its pros and cons
• AI Guide by Mozilla
• Collection of resources related to Applied ML
• List of for MLOps
• Prompt Engineering Playbook for Programmers

Free courses

• Fast AI by Jeremy Howard
• AI Canon - List of resources around GPT
• Free Deep learning course

Articles

• My LLM codegen workflow atm
• How to build your own perplexity for any dataset
• How a Machine Learns
• Machine learning is still too hard - Year 2022
• Neural Networks from Scratch
• History of AI
• Machine Learning Algorithms: What is a Neural Network?
• What is Benford’s Law and why is it important for data science?
• Benford’s Law and Financial Statements
• Data Scientists Should Be More End-to-End
• Team Data science process (Microsoft)
• Traits of Good Data Scientist
• The First Rule of Machine Learning: Start without Machine Learning
• Deep learning is hitting wall
• Real world Recommendation System

Videos

• Neural Networks Demystified
• Deep Learning: A Crash course

Vector Embeddings, Vector Databases

• Storing OpenAI embeddings in Postgres with pgvector

ChatGPT, LLMs

• A practical guide to building successful LLM products.
• Emerging Architecture for LLM Applications
• LocalGPT - Chat with your documents on your local device using GPT models
• Run LLMs from command line
• Resources on LLMs

AI based Translation

• Lokalize - AI based translation of file
• Vibery - Semantic Search using embeddings and KNN

Tools

• Markitdown - Convert PDF and Office documents to markdown to feed into LLM
• Aider - AI pair programming in your terminal
• An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena
• Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more.
• Vespa is an open-source search engine and big data processing platform. It’s particularly well[1]suited for applications that require low latency and high throughput. Our teams like Vespa’s ability to implement hybrid search using multiple retrieval techniques, to efficiently filter and sort many types of metadata, to implement multi-phased ranking, to index multiple vectors (e.g., for each chunk) per document without duplicating all the metadata into separately indexed documents and to retrieve data from multiple indexed fields at once.
• Kotaemon - An open-source RAG-based tool for chatting with your documents.