Week 39
5 Minutes of Data Science - week 39
Highlights from September 26 to October 02
Foreword
Last week, there was focus on environmentally friendly machine learning from Google AI and Amazon Science. Also, there was a text-to-video model released last week. More info below 👇
Come say hi on Twitter and see you next week.
Blogs
- Quantization for Fast and Environmentally Sustainable Reinforcement Learning, by Google AI
- DALL·E Now Available Without Waitlist, by Open AI
- Amazon sponsors contest on energy management in buildings, by Amazon Science
- Data-driven fault analysis is key to sustainable facilities management, by Amazon Science
- Amazon Scholar Rupak Majumdar wins CONCUR Test-of-Time Award, by Amazon Science
- The science behind Alexa’s new interactive story-creation experience, by Amazon Science
- The science behind Amazon’s spatial audio-processing technology, by Amazon Science
- Amazon Halo Rise advances the future of sleep, by Amazon Science
- Alexa’s text-to-speech research at Interspeech 2022, by Amazon Science
- Scaling to trillion-parameter model training on AWS, by Amazon Science
Podcasts
- LIDAR, cameras and autonomous vehicles (Ep. 204), by Data Science At Home
- David Hirko - AI observability and data as a cybersecurity weakness, by Towards Data Science
- Production data labeling workflows, by Practical AI
- Applied AI/ML Research at PayPal with Vidyut Naware - #593, by The TWIML AI
- Responsible and Explainable AI - Supreet Kaur, by Data Talks
Youtube
- Have you seen more math videos in your feed recently? (SoME2 results), by 3Blue1Brown
- Unlocking the mystery of the demon-duck of doom - Unfolded, by DeepMind
- Welcome to DeepMind: Embarking on one of the greatest adventures in scientific history, by DeepMind
- Persuasion is key in DS, at r/Data Science (💬25)
- I started out as an in-house data scientist and then moved on to management consulting. Here are 10 tips that have helped me greatly in business., at r/Data Science (💬62)
- "Do I need to know {insert advanced math} to get a Data Science job?" [Rant], at r/Data Science (💬138)
- [D] Types of Machine Learning Papers, at r/Machine Learning (💬80)
- [P] stablediffusion-infinity: Outpainting with Stable Diffusion on an infinite canvas, at r/Machine Learning (💬44)
- [P] Pokémon text to image, fine tuned stable diffusion model with Gradio UI, at r/Machine Learning (💬30)
- The Bayesian vs Frequentist debate, at r/Ask Statistics (💬22)
- Are Bayesianism and frequentism alternative interpretations of probability or different models altogether?, at r/Ask Statistics (💬16)
- Why use ARIMA over ARMA?, at r/Ask Statistics (💬6)
- An AI that generates videos from text! | Make-A-Video Explained, at r/Latest in ML (💬1)
- How can I keep up with AI research and development? [Twitter accounts to follow], at r/Latest in ML (💬0)
- How can I keep up with AI research and development? [Twitter accounts to follow], at r/Latest in ML (💬0)
Github jupyter notebook trends
- Dreambooth-Stable-Diffusion: Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
- whisper: Robust Speech Recognition via Large-Scale Weak Supervision
- stability-sdk: SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)
- Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.
- UnstableFusion: A Stable Diffusion desktop frontend with inpainting, img2img and more!
- data-engineering-zoomcamp: Free Data Engineering course!
- machine-learning-engineering-for-production-public: Public repo for DeepLearning.AI MLEP Specialization
- BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
- PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks
- mlops-course: A project-based course on the foundations of MLOps with a focus on intuition and application.
- 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.
- examples: Deep Learning Examples
- VToonify: [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
- ml-course: Open Machine Learning course
- notebooks: Notebooks using the Hugging Face libraries🤗
- sample-generator: Tools to train a generative model on arbitrary audio samples
- AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics: Implementation/Tutorial of Automated Machine Learning (AutoML) methods for static/batch and online data analytics
- fastbook: The fastai book, published as Jupyter Notebooks
- pyprobml: Python code for "Probabilistic Machine learning" book by Kevin Murphy
- keras-io: Keras documentation, hosted live at keras.io
- annotated-transformer: An annotated implementation of the Transformer paper.
Github python trends
- stablediffusion-infinity: Outpainting with Stable Diffusion on an infinite canvas
- lama-cleaner: Image inpainting tool powered by SOTA AI Model
- scipy: SciPy library main repository
- lightning: Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.
- alphafold: Open source code for AlphaFold.
- WSL: Issues found on WSL
- system-design-primer: Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
- kitty: Cross-platform, fast, feature-rich, GPU based terminal
- CodeFormer: PyTorch codes for "Towards Robust Blind Face Restoration with Codebook Lookup Transformer" (NeurIPS 2022)
- yolov5: YOLOv5🚀in PyTorch > ONNX > CoreML > TFLite
Have a great week!
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