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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