Week 43
5 Minutes of Data Science - week 43
Highlights from October 24 to October 30
Foreword
Hi everyone - especially to the new subscribers! 👋 So great to have you here!
I’m planning on adding more feeds to this newsletter. That includes more feeds of blogs from known data scientists, youtube channels, podcasts, research and other data science newsletters.
Remember that this newsletter is open source (repo) and you’re free to suggest any feeds. Or just come say hi on Twitter.
Blogs
- Natural Language Assessment: A New Framework to Promote Education, by Google AI
- Open Images V7 — Now Featuring Point Labels, by Google AI
- Amazon SCOT announces 2022 INFORMS Scholars, by Amazon Science
- reMARS revisited: Autonomous mobile robots and safety design, by Amazon Science
- Transferring depth estimation knowledge between cameras, by Amazon Science
- reMARS revisited: Building AI-enabled perception for robots, by Amazon Science
- The breadth of Amazon’s computer vision research is on display at ECCV, by Amazon Science
- Amazon Robotics hosted Day One Fellowship Summit, by Amazon Science
- The quest to deploy autonomous robots in fulfillment centers, by Amazon Science
- Non-Autoregressive Neural Machine Translation: A Call for Clarity, by Apple Machine Learning
- Prompting for a Conversation: How to Control a Dialog Model?, by Apple Machine Learning
- A Treatise On FST Lattice Based MMI Training, by Apple Machine Learning
Podcasts
- Private machine learning done right (Ep. 207), by Data Science At Home
- Tribal Marketing, by Data Skeptic
- AI adoption in large, well-established companies, by Practical AI
- Live from TWIMLcon: AI Platforms 2022 - You’re not Facebook. Architecting MLOps for B2B Use Cases with Jacopo Tagliabue - #596, by The TWIML AI
- Large-Scale Entity Resolution - Sonal Goyal, by Data Talks
- Data = Oil, at r/Data Science (💬77)
- PYTHON CHARTS: a new visualization website feaaturing matplotlib, seaborn and plotly [Over 500 charts with reproducible code], at r/Data Science (💬30)
- Data Science Book Club, at r/Data Science (💬122)
- Modern Disney Diffusion, dreambooth model trained using the diffusers implementation, at r/Machine Learning (💬55)
- TOCH outperforms state of the art 3D hand-object interaction models and produces smooth interactions even before and after contact, at r/Machine Learning (💬16)
- Up to 12X faster GPU inference on Bert, T5 and other transformers with OpenAI Triton kernels, at r/Machine Learning (💬37)
- how to learn statistics if you’re really, really dumb?, at r/Ask Statistics (💬21)
- Resources for translating between DS / ML / Stats speak? (thesaurus?), at r/Ask Statistics (💬7)
- An intuitive explanation for entropy?, at r/Ask Statistics (💬2)
- AI Image Editing from Text! Imagic Explained, at r/Latest in ML (💬1)
Github jupyter notebook trends
- 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.
- stable-diffusion: Latent Text-to-Image Diffusion
- yolov7: Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
- Mubert-Text-to-Music: A simple notebook demonstrating prompt-based music generation via Mubert API
- carefree-creator: AI magics meet Infinite draw board.
- ml-basics: Exercise notebooks for Machine Learning modules on Microsoft Learn
- data: Data and code behind the articles and graphics at FiveThirtyEight
- deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book “Deep Learning with Python”
- Probabilistic-Programming-and-Bayesian-Methods-for-Hackers: aka “Bayesian Methods for Hackers”: An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
- Complete-Python-3-Bootcamp: Course Files for Complete Python 3 Bootcamp Course on Udemy
- tsai: Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
- ColabFold: Making Protein folding accessible to all!
- paper2gui: Convert AI papers to GUI,Make it easy and convenient for everyone to use artificial intelligence technology.
- nerf_pl: NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning
- awesome-notebooks: Ready to use data science templates, organized by tools to jumpstart your projects and data products in minutes.😎published by the Naas community.
- Diffusion-ColabUI: One-click run on Colab for all major models (NovelAI, Stable Diffusion V1.5)
- pythoncode-tutorials: The Python Code Tutorials
Github python trends
- fast-stable-diffusion: fast-stable-diffusion, +25-50% speed increase + memory efficient + DreamBooth
- PaddleNLP: 👑Easy-to-use and powerful NLP library with🤗Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including🗂Text Classification,🔍Neural Search,❓Question Answering,ℹ️Information Extraction,📄Document Intelligence,💌Sentiment Analysis and🖼Diffusion AICG system etc.
- python-cheatsheet: Comprehensive Python Cheatsheet
- nerfstudio: A collaboration friendly studio for NeRFs
- Deep-Learning-Papers-Reading-Roadmap: Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
- yolov5: YOLOv5🚀in PyTorch > ONNX > CoreML > TFLite
- fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
- Poisson_flow: Code for NeurIPS 2022 Paper, “Poisson Flow Generative Models”
See you next Monday!
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