Artificial Intelligence is reshaping your relationship with the world and it's just getting started. Tesla's autopilot, job automation, the products you'stumble upon' online - it's entering our daily lives, careers, businesses, even our homes with such blistering pace you probably haven't even realized it. There's a reason Andrew Ng, the founder of $100m company Coursera said "Artificial Intelligence is the new electricity" - soon it'll be as much a part of your daily life as your smartphone, except without the off button. But here's where things get really crazy. This time round, the revolution will see machines taking on tasks no human intellect could ever perform.

In this series, I will try to give you an overview of the MXnet Deep Learning library: we'll look at its main features and its Python API (which I suspect will be the #1 choice). Later on, we'll explore some of the MXNet tutorials and notebooks available online, and we'll hopefully manage to understand every single line of code! If you'd like learn more about the rationale and the architecture of MXNet, you should read this paper, named "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems". We'll cover most of the concepts presented in the paper, but hopefully in a more accessible way. I'll go as slow and explain as much as I need to.

UK police are trialling a computer system that can piece together what might have happened at a crime scene. The idea is that the system, called VALCRI, will be able to do the laborious parts of a crime analyst's job in seconds, freeing them to focus on the case, while also provoking new lines of enquiry and possible narratives that may have been missed. "Everyone thinks policing is about connecting the dots, but that's the easy bit," says William Wong, who leads the project at Middlesex University London. "The hard part is working out which dots need to be connected." VALCRI's main job is to help generate plausible ideas about how, when and why a crime was committed as well as who did it.

"Learning to Fly by Crashing," a paper from CMU roboticists Dhiraj Gandhi, Lerrel Pinto, and Abhinav Gupta, has such a nice abstract that I'll just let them explain what this research is all about: The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects [. . We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. One way to think of flying (or driving or walking or any other form of motion) is that success is simply a continual failure to crash.

I put together an in-depth tutorial to explain Transforms (Data Augmentation), the Dataset class, and the DataLoader class in Pytorch. I also show a ton of use cases for different transforms applied on Grayscale and Color images, along with Segmentation datasets where the same transform should be applied to both the input and target images. I show how to do Affine transforms (rotation, translation, shear, zoom), some awesome Image-based transforms (saturation, brightness, contrast, gamma, grayscale). These transforms can be applied with pre-determined settings or randomly sampled from a range of values. I also show some cool utility transforms like type casting, converting to tensors, and going from CHW to HWC.

It's a common task for a data scientist: you need to generate segments (or clusters- I'll use the terms interchangably) of the customer base. With definitions, of course!!! Clustering is the subfield of unsupervised learning that aims to partition unlabelled datasets into consistent groups based on some shared unknown characteristics. All the tools you'll need are in Scikit-Learn, so I'll leave the code to a minimum. Instead, through the medium of GIFs, this tutorial will describe the most common techniques. If GIFs aren't your thing (what are you doing on the internet?), You can download this jupyter notebook here and the gifs can be downloaded from this folder (or you can just right click on the GIFs and select'Save image as…'). Clustering algorithms can be broadly split into two types, depending on whether the number of segments is explicitly specified by the user.

In this blog post, I'll help you get started using Apache Spark's spark.ml Classification is a family of supervised machine learning algorithms that identify which category an item belongs to (for example, whether a cancer tissue observation is malignant or not), based on labeled examples of known items (for example, observations known to be malignant or not). Classification takes a set of data with known labels and pre-determined features and learns how to label new records based on that information. Features are the "if questions" that you ask. The label is the answer to those questions.

Last week, my Microsoft colleagues Bharath Sankaranarayan and Carl Saroufim presented a live webinar showing how you can predict a patient's length of stay at a hospital using SQL Server R Services. The recorded webinar is available for on-demand viewing now. The webinar is based on the Machine Learning Solution Template Predicting Length of Stay in Hospitals, which we covered here on the blog back in March. The solution is based on an instance of the Data Science Virtual Machine, which makes it easy to try it yourself. Just click the "Deploy" button to create your own instance in Azure with all of the data and scripts preloaded.

Document classification is a fundamental machine learning task. It is used for all kinds of applications, like filtering spam, routing support request to the right support rep, language detection, genre classification, sentiment analysis, and many more. To demonstrate text classification with scikit-learn, we're going to build a simple spam filter. While the filters in production for services like Gmail are vastly more sophisticated, the model we'll have by the end of this tutorial is effective, and surprisingly accurate. Spam filtering is kind of like the "Hello world" of document classification. However, something to be aware of is that you aren't limited to two classes.