Adding support for polynomials to Numba https://
bit.ly/3N0MZog
#AI #MachineLearning #DeepLearning #LLMs #DataScience
SOFTWARE
-
Numba Adds Polynomial Support for Machine Learning
By
–
-
Space-Integrated Cloud Computing: Satellite Connectivity and Edge
By
–
One Step Beyond! Insights from #Space @MSCloud Join Us Dec 6 https://
bit.ly/3snVz9l 9am PT | 12pm ET | 6pm CET The #future of #cloud will also incorporate space, such as #satellite #connectivity and data, creating an ultimate #edge and enabling computing -
Methods for studying implementing training building AI applications
By
–
How are you studying/implementing/training/building AI apps?
-
Claude Pro Enhanced Controls and File Management Features
By
–
We’ve added enhanced controls for Claude Pro users to improve your experience on https://t.co/uLbS2JNczH. Select which model version of Claude you’d like to power your chat experience, and easily view uploaded files next to your messages. pic.twitter.com/jFeCRCZNXf
— Anthropic (@AnthropicAI) 4 décembre 2023We’ve added enhanced controls for Claude Pro users to improve your experience on http://
claude.ai. Select which model version of Claude you’d like to power your chat experience, and easily view uploaded files next to your messages. -
GPU Programming and Transformers: Essential ML Systems Knowledge
By
–
fewer than 100 people deeply understand both (i) transformers and (ii) the GPU programming model want to learn machine learning? gain some esoteric systems knowledge; spend some time really learning CUDA
-
Python’s Role in Data Science and Machine Learning Infrastructure
By
–
Because for most of the Data Science, ML, and AI use cases almost all of the important stuff happens behind the scenes in highly optimized C, C++, Rust, and CUDA libraries. And these use cases is where Python has by now entrenched itself.
-
New Shortcut for Editing GPTs Directly from Chat
By
–
During a conversation with a GPT, you can now open its menu to find an option to edit the GPT directly. Previously, you would have to navigate to the Explore page and open it from there.
-
NASA Lunar VIPER Mission AI Planning System Success
By
–
During undergrad and after graduation, I spent 3.5 years at @NASA working on the Lunar VIPER mission helping build a system health aware real time planning advisor using the @JuliaLanguage
. It’s so cool to see this coming together for the mission: -
RAG system reduces token input from 128k to 2k
By
–
Exactly!
To complement on the end: thanks to the RAG system, the model was fed around 2k tokens each time, down from the 128k tokens of the original document. -
Platform addresses developer costs for AI-powered applications
By
–
I hear you on this. The team has been thinking about how we can help better enable people like you to build awesome projects like tldraw without having to bear the cost for every user. Stay tuned, thank you for building with us, and trust that we want to solve this for you!