5 Minutes of Data Science - week 38
Highlights from September 19 to September 25
A new podcast feed has been added to the content of this newsletter (Data Science at Home). A reminder that this newsletter is an ETL pipeline and open source. You can suggest any feeds in the issues section.
Good readings and come say hi on Twitter.
- Building safer dialogue agents, by DeepMind
- TensorStore for High-Performance, Scalable Array Storage, by Google AI
- View Synthesis with Transformers, by Google AI
- FindIt: Generalized Object Localization with Natural Language Queries, by Google AI
- Introducing Whisper, by Open AI
- Alexa’s spoken-language-understanding research at Interspeech 2022, by Amazon Science
- Alexa speech science developments at Interspeech 2022, by Amazon Science
- Amazon launches Alexa Prize TaskBot Challenge 2, by Amazon Science
- Jaco Geldenhuys and Willem Visser win ISSTA Impact Paper Award, by Amazon Science
- RecSys 2022: “Recommenders are ubiquitous”, by Amazon Science
- The surprisingly subtle challenge of automating damage detection, by Amazon Science
- Didn’t have to chart this one 🔥, at r/Data Science (💬64)
- Leaked transcript from the meeting where RegEx was invented, at r/Data Science (💬32)
- I found sensitive employee data for a contractor for Walmart. They're not doing anything about it, at r/Data Science (💬92)
- [P] Enhancing local detail and cohesion by mosaicing with stable diffusion Gradio Web UI, at r/Machine Learning (💬31)
- [P] I turned Stable Diffusion into a lossy image compression codec and it performs great!, at r/Machine Learning (💬101)
- [R] META researchers generate realistic renders from unseen views of any human captured from a single-view RGB-D camera, at r/Machine Learning (💬31)
- What is a good beginner book which will teach me mathematical statistics, markov chains and ising models. Optional- sufficiency, exponential family models, at r/Ask Statistics (💬14)
- A Minecraft Question!, at r/Ask Statistics (💬8)
- Why is joint density in the denominator of bayes a valid probability distribution?, at r/Ask Statistics (💬9)
- Linear Least Squared Regression visually explained, at r/Latest in ML (💬0)
Github jupyter notebook trends
- pytorch-deep-learning: Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
- nn-zero-to-hero: Neural Networks: Zero to Hero
- YOLOv6: YOLOv6: a single-stage object detection framework dedicated to industrial applications.
- annotated_deep_learning_paper_implementations: 🧑🏫59 Implementations/tutorials of deep learning papers with side-by-side notes📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...),🎮reinforcement learning (ppo, dqn), capsnet, distillation, ...🧠
- Dreambooth-Stable-Diffusion: Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion
- google-research: Google Research
- homemade-machine-learning: 🤖Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
- diffusionbee-stable-diffusion-ui: Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
- 2022-Machine-Learning-Specialization: None
- Made-With-ML: Learn how to responsibly deliver value with machine learning.
- shap: A game theoretic approach to explain the output of any machine learning model.
- SQL-Data-Analysis-and-Visualization-Projects: SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.
- stablediffusion-infinity: Outpainting with Stable Diffusion on an infinite canvas
- 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
- pycaret: An open-source, low-code machine learning library in Python
- yolov7: Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
- Kalman-and-Bayesian-Filters-in-Python: Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
- vosk-api: Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
- stability-sdk: SDK for interacting with stability.ai APIs (e.g. stable diffusion inference)
- aima-python: Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Github python trends
- stable-diffusion-webui: Stable Diffusion web UI
- CodeFormer: PyTorch codes for "Towards Robust Blind Face Restoration with Codebook Lookup Transformer" (NeurIPS 2022)
- Python: All Algorithms implemented in Python
- awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.
- keras: Deep Learning for humans
- GFPGAN: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
- mmdetection: OpenMMLab Detection Toolbox and Benchmark
- airflow: Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
- transformers: 🤗Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
- ivy: The Unified Machine Learning Framework
- streamlit: Streamlit — The fastest way to build data apps in Python
- gym: A toolkit for developing and comparing reinforcement learning algorithms.
- pandas: Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
See you next week!