October 17, 2022

Week 41

5 Minutes of Data Science - week 41

Highlights from October 10 to October 16

Foreword

This was a busy week - didn't have the time to research the best from below. Enjoy!

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Blogs

• Measuring perception in AI models, by DeepMind
• UL2 20B: An Open Source Unified Language Learner, by Google AI
• Crossmodal-3600 — Multilingual Reference Captions for Geographically Diverse Images, by Google AI
• Solving some of the largest, most complex operations problems, by Amazon Science
• Maximizing the efficiency of Amazon's own delivery networks, by Amazon Science
• Five MIT PhD students named as inaugural Amazon Fellows, by Amazon Science
• 3 questions with Antia Lamas-Linares about the Nobel Prize in physics, by Amazon Science
• Amazon, Virginia Tech announce fellowship and faculty research awards, by Amazon Science
• Using graph neural networks to recommend related products, by Amazon Science
• ECCV 2022, by Apple Machine Learning
• SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks, by Apple Machine Learning
• Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents, by Apple Machine Learning

Podcasts

• Edge AI for applications in military and space (Ep. 206), by Data Science At Home
• Edouard Harris - New Research: Advanced AI may tend to seek power by default, by Towards Data Science
• What's up, DocQuery?, by Practical AI
• Data Science Career Development - Katie Bauer, by Data Talks

Reddit

• Am I stuck in the past? No, its the children who are wrong., at r/Data Science (💬28)
• A reminder that the labor market is heavily a buyer's market. Job has been posted for only 4 minutes and has over 200 applicants. (It uses LinkedIn's Easy Apply, so these should be people who actually did apply rather than just view a webpage). It's crazy out there., at r/Data Science (💬129)
• Is this a normal occurrence?, at r/Data Science (💬69)
• [R] MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model + Gradio Demo, at r/Machine Learning (💬17)
• [P] I built densify, a data augmentation and visualization tool for point clouds, at r/Machine Learning (💬14)
• [R] Neural Networks are Decision Trees, at r/Machine Learning (💬123)
• How to interpret multi variable linear regression analysis, at r/Ask Statistics (💬11)
• Is it true that most statisticians don't use formal tests of normality, but just look at a histogram of the data instead?, at r/Ask Statistics (💬16)
• Switching to Spatial analysis, at r/Ask Statistics (💬2)
• 3D Models from Text! DreamFusion Explained, at r/Latest in ML (💬1)
• A list of Open source tools in Data Centric AI, at r/Latest in ML (💬0)

Github jupyter notebook trends

• VToonify: [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
• stability-sdk: SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)
• course22: The fast.ai course notebooks
• learnopencv: Learn OpenCV : C++ and Python Examples
• BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
• python-machine-learning-book-3rd-edition: The "Python Machine Learning (3rd edition)" book code repository
• pyfolio: Portfolio and risk analytics in Python
• taming-transformers: Taming Transformers for High-Resolution Image Synthesis
• Data-Science-For-Beginners: 10 Weeks, 20 Lessons, Data Science for All!
• pydata-book: Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
• deep-learning-from-scratch: 『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)
• Complete-Python-3-Bootcamp: Course Files for Complete Python 3 Bootcamp Course on Udemy
• disco-diffusion: None
• Python-project-Scripts: This repositories contains a list of python scripts projects from beginner level advancing slowly. More code snippets to be added soon. feel free to clone this repo
• latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models
• data: Data and code behind the articles and graphics at FiveThirtyEight
• stable-diffusion: A latent text-to-image diffusion model
• tensorflow-deep-learning: All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
• pytorch-deep-learning: Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
• handson-ml3: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Github python trends

• stable-diffusion-webui: Stable Diffusion web UI
• FedML: FedML - The federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale. Supporting large-scale cross-silo federated learning, cross-device federated learning on smartphones/IoTs, and research simulation. MLOps and App Marketplace are also enabled (https://open.fedml.ai).
• GFPGAN: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
• face_recognition: The world's simplest facial recognition api for Python and the command line
• jupyterhub: Multi-user server for Jupyter notebooks
• detrex: IDEA Open Source Toolbox for Transformer Based Object Detection Algorithms
• motion-diffusion-model: The official PyTorch implementation of the paper "Human Motion Diffusion Model"
• bbs: Forum for discussing Internet censorship circumvention
• stable-dreamfusion: A pytorch implementation of text-to-3D dreamfusion, powered by stable diffusion.
• transformers: 🤗Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
• PaddleOCR: Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
• nerfstudio: A collaboration friendly studio for NeRFs
• xformers: Hackable and optimized Transformers building blocks, supporting a composable construction.
• tortoise-tts: A multi-voice TTS system trained with an emphasis on quality
• scikit-learn: scikit-learn: machine learning in Python
• ansible: Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems.https://docs.ansible.com.
• mmocr: OpenMMLab Text Detection, Recognition and Understanding Toolbox

Until next Monday!