deep-learning

In this repository we have notebooks and source code used to build the OpenAI x Pinecone Q&A app. You can find more information in our webinar here. Link

We’ll provide you with some examples of how Sagify can simplify and expedite your ML pipelines. You can train, tune and deploy a Machine Learning on the same day by using Sagify! Link

Built on years of experience working with large language models. With one goal, help accelerate their deployment. Link

THE LARGEST AI TOOLS DIRECTORY, UPDATED DAILY Link

The OpenAI Cookbook shares example code for accomplishing common tasks with the OpenAI API. Link

OpenAI v2 Embedding + Search + Context enhanced completions Link

Build GPT-3 powered apps, without writing any code Link

Sous-chef.ai allows users to take photos of ingredients in their fridge/pantry and ask for a user’s food preferences. Using this information, the app provides customized suggested recipes. Link

how to get the best python open source model without overfitting Link

A curated list of resources dedicated to Natural Language Processing Link

Awesome-Pytorch-list Link

ChatGPT came out a few months ago and blew everyones’ minds with its ability to answer questions sourced from a broad set of knowledge. Around the time that ChatGPT was demonstrating how powerful large language models could be, the Dagster core team was facing a problem. Link

The conversational way of generating code with ChatGPT works well with the cell structure of Jupyter Notebooks Link

In this repository, I will share some useful notes and references about deploying deep learning-based models in production. Link

A framework for composing retrieval models and language models into powerful pipelines that tackle knowledge-intensive tasks. Link