​​Data Science Digest readers can get over 20% off on any RE•WORK Summit using code DSDIGEST. Meet with Bengio, Goodfellow and more https://bit.ly/2JXpWfb

Generalized Additive Models in R

This short course will teach you how to use these flexible, powerful tools to model data and solve data science problems. GAMs offer a middle ground between simple linear models and complex machine-learning techniques, allowing you to model and understand complex systems.

http://bit.ly/2InqoRO

Deep Learning Models

A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. This collection has been up for about two weeks and has ~7,000 stars already!

http://bit.ly/2IrofEK

18 Impressive Applications of Generative Adversarial Networks

This post offers a review of a large number of interesting applications of GANs to help you develop an intuition for the types of problems where GANs can be used and useful.

http://bit.ly/31K35JI

Machine learning datasets

A list of the biggest datasets for machine learning from across the web. Computer vision, natural language processing, audio, and medical datasets.

http://bit.ly/2Fp9q3P

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Optimizing your R code – a guided example

Optimizing R code is not always the priority. But when you run out of memory, or it just takes too long, you start to wonder if there are better ways to do things! In this article, the author demonstrates the methods of optimizing R code and the process behind it.

http://bit.ly/2ZFvH4O

Distributed Deep Learning Pipelines with PySpark and Keras

An easy approach to data pipelining using PySpark and doing distributed deep learning with Keras.

http://bit.ly/2ZFao3s

Machine Learning for Everyone

The best general intro article about Machine Learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.

http://bit.ly/2ZGQBRd

Collection of Interactive Machine Learning Examples

Seedbank is a registry and search engine for Colab notebooks for machine learning, enabling rapid exploration and learning. You can browse the site and use, experiment with, and fork Colab notebooks. The forked notebooks are stored in your Google Drive and you can share them just like any other Google Docs.

http://bit.ly/2ZPFWDE

Random Forest vs AutoML

In this article, the author demonstrates how to prepare the data and train the Random Forest model on an Adult dataset with python and scikit-learn. Using the same dataset, he shows how to train Random Forest with AutoML using mljar-supervised.

http://bit.ly/2Ygkmb9

TensorWatch

TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.

http://bit.ly/2YhR8ZG

And Voilà!

In this article, you will learn how Voilà turns Jupyter notebooks into standalone web applications.

http://bit.ly/2YnB74m

​​Webinar «Kubeflow, MLFlow, and Beyond — Augmenting ML Delivery»

Organized in collaboration with #ODSC, this free webinar will provide insights on how to design a reference machine learning workflow, as well as an overview of open source tools used to automate ML workflow.

The attendees will go away with a deeper view of traps and pitfalls they may come across at every stage of ML lifecycle and get a reference implementation and automation of ML Workflow.

When: July 16, 1 pm — 2 pm EST
Speaker: Stepan Pushkarev, CTO of Provectus

Act fast to register: http://bit.ly/MLFlowWebinar
Learn more: https://www.facebook.com/events/1183366371834820

PyTorch Image Models

This repository contains PyTorch image models, noscripts, pre-trained weights: (SE) ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more.

http://bit.ly/2YlZwqW

​​Looking forward to ODS.ai Odessa Meetup & Data Bar taking place July 5 in Odessa. Join the first informal meetup community on Open Data Science in the Odessa region. Lots of networking and heated discussion on the upcoming EECVC conference are guaranteed. The participation is free, but registration is required.

Learn more: http://bit.ly/2YB19B2

Study E-Book

This repository contains a variety of insightful e-books about Computer Vision, Deep Learning, Machine Learning, Math, NLP, Python, and Reinforcement Learning.

http://bit.ly/2Yun110

Best Resources for Getting Started With GANs

In this article, you will learn the best resources you can use to learn about generative adversarial networks: GAN Applications, GAN Video Presentations, GAN Paper Reading List, GAN Books.

http://bit.ly/2YuEF51

Accelerating MRI reconstruction via active acquisition

In this article, you will learn how researchers from Facebook AI proposed a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.

http://bit.ly/2YyT3ZK

R Functions Tutorial

In this tutorial, you will look closely at different types of functions in R to learn how they work, and why they're useful for data science and data analysis tasks.

http://bit.ly/2YF7Br6

Introducing MASS

In this article, you will learn about MASS, a new pre-training method that’s claimed to achieve better results than BERT and GPT.

http://bit.ly/2YHxdDK