Artificial Intelligence (AI) pioneer Nvidia has announced it will train 100,000 developers in "deep learning" to bolster health care research and improve treatment in diseases like cancer. Deep learning is Nvidia's term for machine learning, the idea of pushing computers to learn the way a human would in order to progress what many are calling the next revolution in technology – machines that "think" like humans. Over the past decade, it's given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In the past, cancer research institutes have looked into using Nvidia's latest advances in AI and deep learning to help pathologists with their overwhelming tasks. One project, Led by Andrew Beck, associate professor of pathology and director of bioinformatics at BIDMC, used an Nvidia Tesla K80 GPU supercomputer chip to speed up the process of training their computational models in breast cancer diagnosis.

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.

New and innovative technologies enable a variety of instructional environments that help students overcome many traditional boundaries and constraints to learning. As the classroom becomes more of an abstraction than a physical space, educators and learners embrace a variety of pioneering tech-powered teaching and learning paradigms that will serve students well upon graduation.

"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.

The first is an exciting new Volta-based GPU instance that we think will completely change the face of the AI developer world through a 3x speedup on LSTM training. Second, we are announcing plans to train 100,000 developers through the Deep Learning Institute (DLI) running on AWS. The third is the joint development of tools that enable large-scale deep learning for the broader developer community. AWS is also delivering sessions at GTC including using Apache MXNet training at scale on Amazon EC2 P2 instances and at the edge through the support of NVIDIA's Jetson TX2 platform. The Tesla V100, based on the Volta architecture and equipped with 640 Tensor Cores, provides breakthrough performance of 120 teraflops of mixed precision deep learning performance.

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.