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 AI native software Engineer in 2025 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 Genkit - An open-source framework for building AI-powered apps 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.