October 10, 2022

Week 40

5 Minutes of Data Science - week 40

Highlights from October 03 to October 09

Foreword

My favourites from the previous week:

• make-a-video-pytorch: Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch
• Discovering novel algorithms with AlphaTensor, by DeepMind
• ML Ops in Production, by Data Skeptic
• [R] VToonify: Controllable High-Resolution Portrait Video Style Transfer, _at [r/Machine Learning](https://reddit.com/r/MachineLearning/comments/xyxe8w
• pandas_exercises: Practice your pandas skills!
• latexify_py: Generates LaTeX math description from Python functions.

Come say hi on Twitter!

Pedro Madruga

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Blogs

• How undesired goals can arise with correct rewards, by DeepMind
• Discovering novel algorithms with AlphaTensor, by DeepMind
• AudioLM: a Language Modeling Approach to Audio Generation, by Google AI
• Large Motion Frame Interpolation, by Google AI
• TRIPP explores the potential of VR–powered meditation, by Amazon Science
• The science behind the “Alexa, what should I watch?” feature, by Amazon Science
• Johns Hopkins & Amazon announce fellows, faculty research awards, by Amazon Science
• Amazon releases dataset for complex, multilingual question answering, by Amazon Science
• Amazon and USC name three new ML Fellows, by Amazon Science
• Amazon hosts 2022 US Frontiers of Engineering Symposium, by Amazon Science

Podcasts

• What are generalist agents and why they can change the AI game (Ep. 205), by Data Science At Home
• ML Ops in Production, by Data Skeptic
• Ad Network Tomography, by Data Skeptic
• Amber Teng - Building apps with a new generation of language models, by Towards Data Science
• The Top 10 Reasons to Register for TWIMLcon: AI Platforms 2022!, by The TWIML AI
• From Testing Phones to Managing NLP Projects - Alvaro Navas Peire, by Data Talks

Reddit

• DS feels? DS feels., at r/Data Science (💬39)
• Oh, oh no…, at r/Data Science (💬109)
• Is anyone tired of all the BS elitism about “statistical rigor”, at r/Data Science (💬172)
• [R] VToonify: Controllable High-Resolution Portrait Video Style Transfer, at r/Machine Learning (💬81)
• [R] The Illustrated Stable Diffusion, at r/Machine Learning (💬26)
• [P] You can control inpainting results in StableDiffusion by changing the initial image (github project in comments), at r/Machine Learning (💬9)
• ANOVA - Why bother if you can run post-hoc t-tests with corrections?, at r/Ask Statistics (💬10)
• Alternative to ANOVA, at r/Ask Statistics (💬10)
• Is there any benefit to conducting a frequentist and Bayesian analysis side-by-side?, at r/Ask Statistics (💬11)
• OpenAI’s Most Recent Model: Whisper (explained), at r/Latest in ML (💬1)
• Bias Variance trade-off explained 👇, at r/Latest in ML (💬1)

Github jupyter notebook trends

• Dreambooth-Stable-Diffusion: Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion. Tweaks focused on training faces, objects, and styles.
• VToonify: [SIGGRAPH Asia 2022] VToonify: Controllable High-Resolution Portrait Video Style Transfer
• pytorch-deep-learning: Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
• CrossAttentionControl: Unofficial implementation of “Prompt-to-Prompt Image Editing with Cross Attention Control” with Stable Diffusion
• Transformers-Tutorials: This repository contains demos I made with the Transformers library by HuggingFace.
• ml-course: Open Machine Learning course
• BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
• 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.
• ML_course: EPFL Machine Learning Course, Fall 2021
• Dreambooth-Stable-Diffusion: Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
• taming-transformers: Taming Transformers for High-Resolution Image Synthesis
• pydata-book: Materials and IPython notebooks for “Python for Data Analysis” by Wes McKinney, published by O’Reilly Media
• course22: The fast.ai course notebooks
• fastbook: The fastai book, published as Jupyter Notebooks
• deepmind-research: This repository contains implementations and illustrative code to accompany DeepMind publications
• 2022-Machine-Learning-Specialization: None
• pandas_exercises: Practice your pandas skills!

Github python trends

• stable-diffusion-webui: Stable Diffusion web UI
• stablediffusion-infinity: Outpainting with Stable Diffusion on an infinite canvas
• natbot: Drive a browser with GPT-3
• datasets: 🤗The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
• black: The uncompromising Python code formatter
• devops-exercises: Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
• latexify_py: Generates LaTeX math description from Python functions.
• fast-stable-diffusion: fast-stable-diffusion, +25-50% speed increase + memory efficient + DreamBooth
• GFPGAN: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
• transformers: 🤗Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
• xformers: Hackable and optimized Transformers building blocks, supporting a composable construction.
• make-a-video-pytorch: Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch
• rembg: Rembg is a tool to remove images background.
• gradio: Create UIs for your machine learning model in Python in 3 minutes

See ya!