In this video we’ll learn how to create a simple chatbot to question-answer your Notion knowledge base using Typescript, LangChain and Pinecone. Link

Explanations to key concepts in ML Link

ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. Link

Scientists and engineers use diagrams of networks in many different ways. Together with many collaborators I am studying networks with the tools of modern mathematics, such as category theory. You can read blog articles, papers and a book about our research. I am collaborating with the Topos Institute to use the resulting math for scientific computation, such as quickly adaptable models of infectious disease. Link The Mathematics of Networks

OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images. Link

ML development and deployment today suffer from fragmented and siloed infrastructure that can differ by framework, hardware, and use case. Such fragmentation restrains developer velocity and imposes barriers to model portability, efficiency, and productionization. Link

PyXAB is a Python open-source library for X-armed bandit algorithms, a prestigious set of optimizers for online black-box optimization, i.e., optimize an objective without gradients, also known as the continuous-arm bandit (CAB), Lipschitz bandit, global optimization (GO) and bandit-based black-box optimization problems. Link

Build software collaboratively with the power of AI, on any device, without spending a second on setup Link

Machine learning has a wide range of possible applications in almost all industries. Model architecture, performance metrics improvement, and optimization of calculations have always been at the center of attention. At the same time, machine learning has not yet gone through the same stages of standardization process as software development has through the past decades. To this day, the field of machine learning does not have a single generally accepted approach to solving problems in terms of practical use of models.

The following is Edsko de Vries’ overview of The Catsters’ Category Theory videos. Link

TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale. Link

Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Link

Let’s create a zero-shot document question answering system using Flan-ULv2, a powerful encoder-decoder language model from Google based on T5. This system can surpass the performance of other models like GPT-3 and T5 in various downstream tasks. We’ll utilize the chainlang library to build an embedding database, search the database for the most similar documents to a given query, and then use the language model to retrieve the most likely answers.

Build a Chat Application with ChatEngine and OpenAI and ChatGPT integration tutorial. The frontend will consist of ChatEngine for our chat application, Redux Toolkit for our state management, Redux Toolkit Query for making api calls, Hero icons for our Icons and React Router for Navigation. For the backend we will be using Node Js as our runtime, Express Js as our backend framework, and OpenAI for Ai integration with our chat.

In this video we use the new OpenAI gpt-3.5-turbo model to create a ChatGPT application in React. This is one of the fastest GPT models to be released. This video is great for beginners to both React and ChatGPT, and is a great portfolio project. We create a chatbot that allows you to communicate directly with the ChatGPT API, which is a project that can be applied to a vast array of React projects!