5 Minutes of Data Science - week 7

Highlights from February 13 to February 19

Foreword

Invite a friend and come say hi on Mastodon. See you next week!

Newsletters

  • LWiAI Podcast #111 - Mostly ChatGPT Again, plus Google’s Bard, Climate Change, AI Seinfeld, by Last Week in AI
  • Why Deployment is the most important part of Machine Learning, by The AI Edge

Blogs

  • How should AI systems behave, and who should decide?, by Open AI
  • Ten university teams selected for Alexa Prize TaskBot Challenge 2, by Amazon Science
  • A user-controllable framework that unifies style transfer methods, by Amazon Science

Podcasts

  • [RB] Online learning is better than batch, right? Wrong! (Ep. 216), by Data Science At Home
  • Serverless GPUs, by Practical AI
  • AI Trends 2023: Causality and the Impact on Large Language Models with Robert Osazuwa Ness - #616, by The TWIML AI
  • Staff AI Engineer - Tatiana Gabruseva, by Data Talks
  • [RB] Online learning is better than batch, right? Wrong! (Ep. 216), by Data Science at Home

Reddit’s top posts

  • PyGWalker: Turn your Pandas Dataframe into a Tableau-style UI for Visual Analysis, at r/Data Science (💬30)
  • There are too many charlatans on Linkedin posing as Data Scientist. Gone through his profile, not a single mention of his work. Most of the posts are engagement farming. The awards also seems to be suspicious and paid. My main question is who should you follow for quality content ?, at r/Data Science (💬141)
  • Laptop recommendations for data analytics in University., at r/Data Science (💬224)
  • neural cloth simulation, at r/Machine Learning (💬23)
  • Please stop, at r/Machine Learning (💬148)
  • Bing: “I will not harm you unless you harm me first”, at r/Machine Learning (💬247)
  • Navy Pilots not being honest, at r/Ask Statistics (💬19)
  • Something I never understood about Bayesian statistics … are priors a posteriori?, at r/Ask Statistics (💬27)
  • Best way to self-teach statistics?, at r/Ask Statistics (💬3)
  • AI Learns to Walk, Hop, and Roll, at r/Latest in ML (💬0)
  • OpenAI AI-Generated Text Classifier Hands-On Review: ChatGPT’s Own AI Detection - Comparison To Similar Tools, at r/Latest in ML (💬0)
  • CLIP: CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
  • Stock-Prediction-Models: Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
  • stable-diffusion-webui-colab: stable diffusion webui colab
  • stable-diffusion: A latent text-to-image diffusion model
  • lora: Using Low-rank adaptation to quickly fine-tune diffusion models.
  • Prompt-Engineering-Guide: 🐙Guides, papers, lecture, and resources for prompt engineering
  • fastbook: The fastai book, published as Jupyter Notebooks
  • taming-transformers: Taming Transformers for High-Resolution Image Synthesis
  • dsp: 𝗗𝗦𝗣: Demonstrate-Search-Predict. A framework for composing retrieval and language models for knowledge-intensive NLP.
  • amazon-sagemaker-examples: Example📓Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using🧠Amazon SageMaker.
  • pytorch-deep-learning: Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
  • InvokeAI: InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
  • automl: Google Brain AutoML
  • BLIP: PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
  • Machine-Learning-Specialization-Coursera: Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
  • latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models
  • numerical-linear-algebra: Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
  • shap: A game theoretic approach to explain the output of any machine learning model.
  • DeepLearningExamples: State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
  • stable-diffusion: Latent Text-to-Image Diffusion
  • models: Models and examples built with TensorFlow
  • mlbookcamp-code: The code from the Machine Learning Bookcamp book and a free course based on the book
  • ColossalAI: Making big AI models cheaper, easier, and more scalable
  • ControlNet: Let us control diffusion models
  • stable-diffusion-webui: Stable Diffusion web UI
  • youtube-dl: Command-line program to download videos from YouTube.com and other video sites
  • yt-dlp: A youtube-dl fork with additional features and fixes
  • picoGPT: An unnecessarily tiny implementation of GPT-2 in NumPy.
  • mm-cot: Official implementation for “Multimodal Chain-of-Thought Reasoning in Language Models” (stay tuned and more will be updated)
  • DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
  • EdgeGPT: Reverse engineered API of Microsoft’s Bing Chat
  • gpt-2: Code for the paper “Language Models are Unsupervised Multitask Learners”
  • shell_gpt: A command-line interface (CLI) productivity tool powered by OpenAI’s GPT-3 models, will help you accomplish your tasks faster and more efficiently.
  • pytorch-image-models: PyTorch image models, scripts, pretrained weights – ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
  • stablediffusion: High-Resolution Image Synthesis with Latent Diffusion Models
  • GFPGAN: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
  • TTS: 🐸💬- a deep learning toolkit for Text-to-Speech, battle-tested in research and production
  • videos: Code for the manim-generated scenes used in 3blue1brown videos
  • transformers: 🤗Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
  • sd_civitai_extension: All of the Civitai models inside Automatic 1111 Stable Diffusion Web UI

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