This free book aims to provide a panoramic perspective of the wide array of string manipulations that you can perform with R.

If you don’t know who Hans Rosling was, check out one of his TED talks here. This book is such a great read even Bill Gate said it is one of the most important books he’s ever read.

“When asked simple questions about global trends–what percentage of the world’s population live in poverty; why the world’s population is increasing; how many girls finish school–we systematically get the answers wrong.” In this book the authors offers a radical new explanation of why this happens.

Master reinforcement learning and deep reinforcement learning by building intelligent applications using OpenAI, TensorFlow, and Python. Python notebooks can be found here.

Discussion of a vignette created by Paige Bailey for the ROpenSci conference, “Ethical Machine Learning: Spotting and Preventing Proxy Bias”.

Many types of machine learning classifiers are notoriously difficult to interpret. If the model produces a surprising label for any given case, it’s difficult to explain. One approach to this dilemma is the technique known as LIME (Local Interpretable Model-Agnostic Explanations).

This blog post explains how to do distributed deep learning using Horovod on Azure. A RetinaNet object detector is trained on the COCO dataset, with distributed training enabled through Horovod and Batch AI.

This blog post covers the launch of AI Lab – a collection of AI projects designed to help developers explore, experience, learn about and code with the latest Microsoft AI Platform technologies.

Details on how Microsoft fixed the installer for Microsoft R Open on Linux.

Microsoft has released a collection of free data sets in areas such as natural language processing, computer vision, and domain-specific areas.

The paper “Deep Video Portraits” is available here.

The paper “Curiosity-driven Exploration by Self-supervised Prediction” and its source code is available here.

The paper “Image Inpainting for Irregular Holes Using Partial Convolutions” is available here.

The paper “Neural Best-Buddies: Sparse Cross-Domain Correspondence” is available here.

The paper “CleanNet: Transfer learning for scalable image classifier training with label noise” is available here.

The paper “CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training” is available here.

Learn more about what’s important to Data Scientists and where to start when it comes to a DevOps process.

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body. Link to the paper here.