In a previous post, we studied various open datasets that could be used to train a model for pixel-wise semantic segmentation of urban scenes. Here, we take a look at various deep learning architectures that cater specifically to time-sensitive domains like autonomous vehicles. In recent years, deep learning has surpassed traditional computer vision algorithms by learning a hierarchy of features from the training dataset itself. This eliminates the need for hand-crafted features and thus such techniques are being extensively explored in academia and industry. Prior to deep learning architectures, semantic segmentation models relied on hand-crafted features fed into classifiers like Random Forests, SVM, etc.

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The TensorFlow layers module provides a high-level API that makes it easy to construct a neural network. It provides methods that facilitate the creation of dense (fully connected) layers and convolutional layers, adding activation functions, and applying dropout regularization. In this tutorial, you'll learn how to use layers to build a convolutional neural network model to recognize the handwritten digits in the MNIST data set. The MNIST dataset comprises 60,000 training examples and 10,000 test examples of the handwritten digits 0–9, formatted as 28x28-pixel monochrome images. Let's set up the skeleton for our TensorFlow program.

I recently recorded an introduction to the Microsoft Artificial Intelligence suite of tools and services you can use in your organization, from what is already built into Microsoft applications you own, through leveraging Cognitive Services, customizing AI, all the way through writing your own AI with Machine Learning, Deep Learning, and Neural Networks. I also explained how to run these services, or host the ones you write on Microsoft Azure using web apps, Docker, and other technologies. And I have a full article on these technologies that you can read here. We have a whole series of these webinars - you can find the list here.

Source: Is healthcare ready for AI, potentially the biggest innovation since the vaccine? Artificial intelligence (AI) has permeated into many parts of everyday life, if you look hard enough you can see it in unassuming places; from the facial recognition of Google photos to the spam filter in your favourite email app. But perhaps it is in areas of commerce and industry where we can see its value the most. Manufacturing operations are being optimised, data is being collated and analysed at an ever increasing rate, and deep learning enables'intelligent' machines to make independent decisions without human guidance. So why is Healthcare, an industry worth trillions, not following suit?

The course targets designers or developers who work with fintech.

In today's dose of inspiration, I wanted to give you an insight into one of the most innovative technologies of the past few years which has the potential to revolutionise entire industries: machine learning. If you want to be a startup on the cutting edge of buzzwords, you have two major avenues right now. The first is "blockchain", which anyone who has seen the bubble of the cryptocurrency market rise can attest to (side note: I am amazed that a cryptocurrency designed after the internet Doge meme to show the silliness of cryptocurrencies is now worth more than $1 billion). And this is where many of most innovative advanced in technology are happening right now, as it allows software to "learn" how to complete complex tasks which previously only humans could do. Not to mention the fact that it can beat humans at the own games, and even display aspects of creativity.

Machine Vision has come a long way since Imagenet, a large repository of labelled images that researchers use to train their newest Artificial Intelligence (AI) agents with, was released, but still to this day images with bad, tricky or just plain weird lighting can still confuse even the best AI's algorithms and get them to misreport whatever it is they're looking at. And there are a multitude of examples where an AI has been tricked or confused, such as an AI that was being used to run an autonomous train prototype that mistook a shadow for a rock and came to a dead stop on the track, and even Nvidia's DAVE 2.0 self-driving car software that under certain lighting conditions would send a simulated car off a cliff, both of which exemplify the issue that machine vision enthusiasts everywhere still face. Over the past couple of years in order to try to overcome the issue researchers have either tried to create special hand crafted rules about how light interacts with objects or used data sets that cover as many lighting situations as possible, but there is a nearly limitless combination of items and light in the real world and that handicaps both approaches. Now though a paper by researchers from MIT and DeepMind has detailed a new AI process that can identify images in different lighting without having to hand craft new rules or train on a huge data set. The process, called a Rendered Intrinsics Network, or RIN for short, automatically separates an image into reflectance, shape, and lighting layers.

Watch a replay of NVIDIA CEO Jensen Huang's keynote address at the GPU Technology Conference 2018 in Silicon Valley, where he unveiled a series of advances to NVIDIA's deep learning computing platform that deliver a 10x performance boost on deep learning workloads; launched the Quadro GV100 GPU, transforming workstations with 118.5 TFLOPS of deep learning performance; introduced NVIDIA DRIVE Constellation to run self-driving car systems for billions of simulated miles, and much more.

Reprinted with permission from HPCWire. Petaflop per second deep learning training performance on the Cori supercomputer at Berkeley Lab's National Energy Research Scientific Computing Center (NERSC) has given climate scientists the ability to use machine learning to identify extreme weather events in huge climate simulation datasets. Predictive accuracies ranging from 89.4% to as high as 99.1% show that trained deep learning neural networks (DNNs) can identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called atmospheric rivers (Figure 1). Figure 1: Relation between ground truth (green boxes) and classification plus regression results (red boxes) of the DNN trained to recognize atmospheric phenomena. The strong relationship between ground truth and the neural network prediction can be seen in the classification plus regression results reported by Berkeley Lab climate scientist Michael Wehner at the Intel Developer Conference held during SC17 last November in Denver, Colorado. Supercomputers like NERSC's Cori system provide scientists with an extraordinary tool to model climate change significantly faster and far more accurately than was possible on previous generation supercomputers.