AI Contents Archive

We’ve released ImageNetX: a set of human annotations for the popular ImageNet benchmark to gauge model robustness strengths/weaknesses — one of the first large scale efforts to pinpoint mistake types in AI computer vision systems. Explore the dataset ⬇️ https://t.co/MtUJpM21cp

Check out The Data Cards Playbook, a self-guided toolkit that promotes practices to improve model and dataset transparency so that teams across organizations can develop responsible ML systems and datasets. Learn more and try out yourself → https://t.co/Zf41cgMReI https://t.co/1ZEA4Jt3qC

Geocoding in Python: A Complete Guide A step-by-step tutorial on geocoding with Python #Python #KDnuggets https://t.co/hQiWPAaKer https://t.co/0Jy9zgswti

Watch the 7th episode of #MeetAGoogleResearcher, where @drewcalcagno speaks with researchers Vivian Yang and John Anderson to discuss their work on wildfire simulation to better understand and react to these disasters ↓ https://t.co/5CSsEezA6K

9 Free Resources to Master Python Python is the most popular general-purpose language and you can learn it for free. #Python #programming #KDnuggets https://t.co/vCB2RfeciZ https://t.co/e4F9Y0Qxsv

Even today’s best #deeplearning vision systems can fail when pose/lighting/background vary. Existing benchmarks show challenging examples but don’t explain why. ImageNetX includes annotations for the entire ImageNet1k validation set + a random subset of 12k training images.

How to deploy #MachineLearning/#DeepLearning models to the web #KDnuggets https://t.co/Qu7GXSi26n

Python Tuples and Tuple Methods #KDnuggets https://t.co/pG6nWMcGRv

All you need to know about text preprocessing for #NLProc and #MachineLearning #KDnuggets https://t.co/0mIvmmQTRA

Essential Linear Algebra for #DataScience and #MachineLearning #KDnuggets https://t.co/Yo55oHHGb4

.@huggingface Machine Learning Demos can now be accessed through @arxiv Hugging Face Spaces is integrated with arXivLabs through a Demo tab that includes links to demos created by the community or the authors themselves. blog: https://t.co/Tl7Pld0xGx https://t.co/FYkWLGD1fR

MIT: Solving brain dynamics gives rise to flexible #machinelearning models https://t.co/J5UpEuM6NN @pierrepinna @sallyeaves @PawlowskiMario @Xbond49 @gvalan @SpirosMargaris @JimMarous @HaroldSinnott @mikeflache @Shi4Tech @TheRudinGroup @ahier #AI #DeepLearning #Fintech #5G https://t.co/kv8kVwnaio

Join us for our virtual NVIDIA Special Address at #CES2023 on Tuesday, Jan. 3 at 8 a.m. PT. We'll be sharing the latest innovations made possible by accelerated computing. https://t.co/oVB53Jb05p

setup a @Gradio demo for dreambooth models on @huggingface model hub in two lines of code example with @Nitrosocke model import gradio as gr gr.Interface.load("models/nitrosocke/Nitro-Diffusion").launch() demo: https://t.co/900sJf6W1O demo code: https://t.co/bTYYCVDdo3 https://t.co/C6tVBJmL46

5 important iPhone security features you should know about https://t.co/cdREaD1rz3 #DataSecurity #Privacy #100DaysOfCode #CloudSecurity #MachineLearning #Phishing #Ransomware #Cybersecurity #CyberAttack #DataProtection #DataBreach #Hacking #security