For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing vast amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach. Companies like Google, Facebook and Microsoft have poured money into deep learning. And the technology's perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars.

Consumer GPU's are good enough, right? Do I need to go on-premises for training? Will my developers really notice the difference between one GPU platform versus the other? All great questions, and everyone mulls these over at some point. Like anything, it's often helpful to consult with a peer who's done it before.

This post is co-authored by Mary Wahl, Data Scientist, Xiaoyong Zhu, Program Manager, Siyu Yang, Software Development Engineer, and Wee Hyong Tok, Principal Data Scientist Manager, at Microsoft. Object detection powers some of the most widely adopted computer vision applications, from people counting in crowd control to pedestrian detection used by self-driving cars. Training an object detection model can take up to weeks on a single GPU, a prohibitively long time for experimenting with hyperparameters and model architectures. This blog will show how you can train an object detection model by distributing deep learning training to multiple GPUs. These GPUs can be on a single machine or several machines.

Using raw data from the entirety of a patient's electronic health record, Google researchers have developed an artificial intelligence network capable of predicting the course of their disease and risk of death during a hospital stay, with much more accuracy than previous methods. The deep learning models were trained on over 216,000 deidentified EHRs from more than 114,000 adult patients, who had been hospitalized for at least one day at either the University of California, San Francisco or the University of Chicago. For those two academic medical centers, the AI predicted the risks of mortality, readmission and prolonged stays, as well as discharge diagnoses, by ICD-9 code. The network was 95% accurate in predicting a patient's risk of dying while in the hospital--with a much lower rate of false alerts--than the traditional regressive model--the augmented Early Warning Score--which measures 28 factors and was about 85% accurate at the two centers. The researchers' findings were published last month in the Nature journal npj Digital Medicine.

For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing massive amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach. Companies like Google, Facebook and Microsoft have poured money into deep learning. And the technology's perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars.

Gluon and Apache MXNet have been great for deep learning, especially for newbies like me. They added a Deep Learning Toolkit that is easy to use and has a number of great pre-trained models that you can easily use to do some general use cases around computer vision. So, I have used a simple well-documented example that I tweaked to save the final image and send some JSON details via MQTT to Apache NiFi. GluonCV makes this even easier! Again, let's take a simple Python example, tweak it, run it via a shell script, and send the results over MQTT.

While I did learn a lot of maths while doing my engineering degree, I forgot most of it by the time I wanted to get into Machine Learning. After I graduated I never really had a need for any of the maths. I did a lot of web programming which relied on logic and I can honestly say that with each system with the word'Management' in the title I lost a third of my math knowledge! I've programmed extensions for Learning Management Systems, Content Management Systems and Customer Relationship Management Systems -- I'll leave you to figure out how much math apptitude I had after working with these systems. At the moment I've got good data science skills and can use a variety of ML and DL algorithms.

A team of Nvidia researchers this week are demonstrating how they've used deep learning to tackle a common challenge: producing a slow-motion video with existing video footage. Nvidia AI system fills in missing frames between the left and right images (in green borders), enabling high quality video play back in slow motion. The research team, presenting their paper at the 2018 Conference on Computer Vision and Pattern Recognition (CVPR), developed a deep learning-based system that can produce slow-motion videos -- slowed down to any frame rate -- from a 30-frame-per-second video. The result is a high-quality video that looks smooth and seamless in comparison to the existing state-of-the-art methods. Check out the video below.

In today's blog post you are going to learn how to perform face recognition in both images and video streams using: As we'll see, the deep learning-based facial embeddings we'll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading! Inside this tutorial, you will learn how to perform facial recognition using OpenCV, Python, and deep learning. We'll start with a brief discussion of how deep learning-based facial recognition works, including the concept of "deep metric learning". From there, I will help you install the libraries you need to actually perform face recognition. Finally, we'll implement face recognition for both still images and video streams. As we'll discover, our face recognition implementation will be capable of running in real-time.

Even though the libraries for R from Python, or Python from R code execution existed since years and despite of a recent announcement of Ursa Labs foundation by Wes McKinney who is aiming to join forces with RStudio foundation, Hadley Wickham in particularly, (find more here) to improve data scientists workflow and unify libraries to be used not only in Python, but in any programming language used by data scientists, some data professionals are still very strict on the language to be used for ANN models limiting their dev. As a continuation of my R vs. Python comparison, I decided to test performance of both languages in terms of time required to train a convolutional neural network based model for image recognition. As the starting point, I took the blog post by Dr. Shirin Glander on how easy it is to build a CNN model in R using Keras. It is a Python library for artificial neural network ML models which provides high level fronted to various deep learning frameworks with Tensorflow being the default one. Since Keras is written in Python, it may be a natural choice for your dev.