The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model. TFUG Taipei hosted Python JAX Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab. Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.

Let's explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being.

In 2012, few people at Microsoft outside of research were working on or talking about machine learning and artificial intelligence, and "data scientist" was not an official job title. Fast-forward eight years, and we have hundreds of data scientists and thousands of engineers building ML and AI into products and services. To help support these employees, we have a thriving 7,000-person internal machine learning community where our employees learn, share, and connect with one another. Here's how we got there. Some might approach the notion of community as a platform or a piece of technology.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.