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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How to Choose the Right Chart Type - https://activewizards.com/blog/how-to-choose-the-right-chart-type-infographic/
This article presents an infographic which shows the possible chart types you can use depending on the data you have.

Scaled Machine Learning Conference 2019 - https://www.youtube.com/playlist?list=PLRM2gQVaW_wWXoUnSfZTxpgDmNaAS1RtG

How to Version Control Jupyter Notebooks - https://nextjournal.com/schmudde/how-to-version-control-jupyter
Jupyter notebooks integrate metadata, source code, formatted text, and rich media into a single document, which makes them poor candidates for conventional version control systems. This article explores a variety of ways to version control your notebooks, including built-in solutions and external tools.

Which Deep Learning Framework is Growing Fastest?

To answer that question, I looked at the number of job listings on Indeed, Monster, LinkedIn, and SimplyHired. I also evaluated changes in Google search volume, GitHub activity, Medium articles, ArXiv articles, and Quora topic followers. Overall, these sources paint a comprehensive picture of growth in demand, usage, and interest.

Link: https://towardsdatascience.com/which-deep-learning-framework-is-growing-fastest-3f77f14aa318

Open Questions about Generative Adversarial Networks

Problem 1: What are the trade-offs between GANs and other generative models?
Problem 2: What sorts of distributions can GANs model?
Problem 3: How can we Scale GANs beyond image synthesis?
Problem 4: What can we say about the global convergence of the training dynamics?
Problem 5: How should we evaluate GANs and when should we use them?
Problem 6: How does GAN training scale with batch size?
Problem 7: What is the relationship between GANs and adversarial examples?

Link: https://distill.pub/2019/gan-open-problems/

Visualising Model Response with easyalluvial

This article will show how you can use alluvial plots to visualise model response in up to 4 dimensions. easyalluvial generates an artificial data space using fixed values for unplotted variables or uses the partial dependence plotting method. It is model agnostic but offers some convenient wrappers for caret models.

Link: https://www.datisticsblog.com/2019/04/visualising-model-response-with-easyalluvial/

Essential Guide to keep up with AI/ML/CV

These fields are booming these days. In order not to become rusty, one has to constantly follow the updates. Here is the essential guide on how to keep up with the important news/papers/discussions/tutorials. This guide is by no means an exhaustive one so contributions are truly welcome.

Link: https://github.com/BAILOOL/DoYouEvenLearn

**Random Forests for Complete Beginners**

The definitive guide to Random Forests and Decision Trees. You will learn what Random Forests are and how they work from the ground up.

Link: https://victorzhou.com/blog/intro-to-random-forests/

Forecasting: Principles and Practice

This interactive textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.

Link: http://bit.ly/2KXjtlS