UK Releases 130 Terabytes of Oil and Gas Data - https://www.spe.org/en/jpt/jpt-article-detail/?art=5282
The dataset covers 12,500 offshore wellbores, 5,000 seismic surveys, and 3,000 pipelines over more than five decades. The release is intended to help promote new investment, technology, and exploration activity on the UK Continental Shelf, ultimately boosting recovery.

Linear Regression - Visualized - https://youtu.be/3g-e2aiRfbU
This series of short videos make linear regression visually intuitive and provide great explanations of specific details.

Deep Learning: State of the Art - https://youtu.be/53YvP6gdD7U
Lex Fridman's 2nd lecture from his Deep Learning course at MIT is a great overview of the cutting edge in deep learning. It introduces a wide variety of applications in natural language processing, AutoML, use of synthetic data, image synthesis, semantic segmentation, etc. Includes a clickable outline that links to each section of the lecture.

Dive into Deep Learning - http://d2l.ai/
An interactive deep learning book with code, math, and immersive discussions.

Best Deals in Deep Learning Cloud Providers - https://towardsdatascience.com/maximize-your-gpu-dollars-a9133f4e546a
Nice comparison of GPU cloud service providers, including specific things to look for, capabilities, costs, and performance.

PySyft Step-By-Step Tutorial - https://github.com/OpenMined/PySyft/tree/master/examples/tutorials
This step-by-step notebook tutorial is an easy introduction to privacy preserving, decentralized deep learning. Here's how to do things like run ML spellcheck on encrypted email.

Jupyter Lab: Evolution of the Jupyter Notebook - https://towardsdatascience.com/jupyter-lab-evolution-of-the-jupyter-notebook-5297cacde6b
An overview of JupyterLab, the next generation of the Jupyter Notebook.

Production AI Conference - https://www.aibooster.com.ua/productionai2019/
First technical and practical conference about implementing models in high-load projects with limited computational resources and high requirements. Only technical speeches and high-professional experts. Speakers from MindGeek(PornHub), Reflect(even Elon Musk twitted about it), Neuromation, WIX, DatAI, AltexSoft, People.ai. Including practical workshops where you can try new technology right away!

Details: https://www.facebook.com/events/232502780882703/

Machine Learning Serving cluster - https://github.com/Hydrospheredata/hydro-serving
Hydrosphere Serving enables you to get your models up and running in an instant, on just about any infrastructure and using any of the available machine learning toolkits.

Pandaral·lel - https://github.com/nalepae/pandarallel
A simple and efficient tool to parallelize your Pandas operations on all your CPUs.

Frameworks for Machine Learning Model Management - https://www.inovex.de/blog/machine-learning-model-management/
This article compares three different tools developed to support reproducible machine learning model development: MLFlow, DVC and Sacred.

Mathematics Dataset - https://github.com/deepmind/mathematics_dataset
This dataset code generates mathematical question and answers pairs, from a range of question types at roughly school-level difficulty. This is designed to test the mathematical learning and algebraic reasoning skills of learning models.

@like A Visual Exploration of Gaussian Processes - https://distill.pub/2019/visual-exploration-gaussian-processes/
This article will show how to turn a collection of small building blocks into a versatile tool for solving regression problems.

GANSynth: Making music with GANs - https://magenta.tensorflow.org/gansynth
In this article, you will learn about GANSynth, a method for generating high-fidelity audio with Generative Adversarial Networks (GANs).

Kubeflow Times Machine Learning — Reproducibility Step by Step - https://medium.com/hydrosphere-io/train-and-deliver-machine-learning-models-to-production-with-a-single-button-push-a6f89dcb1bfb
This article will show a way to create a pipeline that connects machine learning workflow steps (like collecting & preparing data, model training, model deployment and so on) into a single reproducible run, which you can execute with a single button push.

Securing a dockerized plumber API with SSL and Basic Authentication - https://qunis.de/how-to-make-a-dockerized-plumber-api-secure-with-ssl-and-basic-authentication/
In this tutorial, you will learn how to run your R code with a plumber API inside a Docker container.

Another 10 Free Must-Read Books for Machine Learning and Data Science - https://www.kdnuggets.com/2019/03/another-10-free-must-read-books-for-machine-learning-and-data-science.html
In this article, you will find a few books on elementary machine learning, a few on general machine topics of interest such as feature engineering and model interpretability, an intro to deep learning, a book on Python programming, a pair of data visualizations entrants, and twin reinforcement learning efforts.

Six Easy Ways to Run Your Jupyter Notebook in the Cloud - https://www.dataschool.io/cloud-services-for-jupyter-notebook/
This article will review six services you can use to easily run your Jupyter notebook in the cloud. All of them have the following characteristics: they don't require you to install anything on your local machine; they are completely free (or they have a free plan); they give you access to the Jupyter Notebook environment; they allow you to import and export notebooks using the standard .ipynb file format; they support the Python language (and most support other languages as well).

Computer Vision Tutorial: A Step-by-Step Introduction to Image Segmentation Techniques - https://www.analyticsvidhya.com/blog/2019/04/introduction-image-segmentation-techniques-python/
In this article, you will learn the concept of image segmentation. It is a powerful computer vision algorithm that builds upon the idea of object detection and takes us to a whole new level of working with image data.

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