It was summer 2017 when Cole, one of our Machine Learning Engineers, first started flying these words around the Dessa office. The team was discussing dream projects to work on within the field of deep learning, and a quick survey of the room confirmed our initial suspicions… to work on something space-related would be pretty freaking awesome! Luckily, we found ourselves in the right place at the right time. The company had recently started a program for employees to dedicate a portion of working hours to personal projects that would help us advance our machine learning knowledge. We knew this program would help advance our product, too.

Subscribe to stay notified about new videos: http://3b1b.co/subscribe Additional funding provided by Amplify Partners. Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy

It might be a bit early to call generative adversarial networks (GANs) the next platform for AI frameworks, but there is little doubt we will hear much more about this beefed up approach to deep learning over the next year and beyond. Like so many other areas where GPUs have a play, much of the early production work is being done in graphics--image and video. This certainly means gaming, which is music to Nvidia's ears. But as more research emerges, especially from the edges of medicine, it is becoming clear that GANs might be able to go several steps beyond mere recognition and actually generate examples, learn from those (through AI "arguments" between a generator and discriminator), and produce some surprising results. There is much to be said about GANs from a future applications perspective, but this is one area where even the best general purpose hardware might not be up to the task at any kind of scale.

Quantum computing is a relatively modern technology that pioneering scientists, researchers, and entrepreneurs worldwide are actively seeking to commercialize. For example, recently at CES (Consumer Electronics Show) in January 2019, IBM debuted its "Q System One" as the first standalone quantum computer geared for scientific and commercial use. Making quantum computing accessible will help accelerate progress in artificial intelligence (AI). Speed up computing, and you enhance the performance of deep learning. For example, the parallel processing capabilities of GPUs (graphics processing units) helped accelerate AI deep learning by providing greater computational power than serial processing CPUs (central processing units) to process large amounts of big data used for machine learning.

I'm super happy to introduce my side project called: deepy. Here is the code: https://github.com/kaszperro/deepy The purpose was to learn how neural networks, autograd and other mechanisms work. Also I think, that those who want to know how mainstream libraries work, it's good starting point, because the code is very small and simple. It's still in early stage though, but so far I've programmed: It uses numpy for computations.

I remember having written a response that was specifically focused on Andrew Ng's Deep Learning training that was launched with a lot of fanfare in October last year. I have added excepts from my Quora answer here and there and this is me just visiting my own answers based on my year long experience since June 2017 working with CEOs and Chair(wo)men of large enterprises, training about 9000 people in my classical (meaning hands-on workshops where we learn the old fashioned way face-to-face) and interacting with tens of thousands of learners worldwide. I will however be brutally honest about my initial observation of the first 1.5 weeks -- which I went through yesterday with great anticipation and truly enjoyed (still enjoying!), of what I experienced. This may actually not have anything to do with his capabilities or intentions rather it("the dilemma") owes this to latest trend (pretty much close to madness) of packing a deep learning course in a MOOC and try to teach to folks everything in bunch of nutshells. I'll get to that in a minute, but first my quick analysis of who this Deep Learning course / specialization may or may not be for. So, who might this course be for?

Machine learning on graphs is a difficult task due to the highly complex, but also informative graph structure. This post is the second in a series on how to do deep learning on graphs with Graph Convolutional Networks (GCNs), a powerful type of neural network designed to work directly on graphs and leverage their structural information. In my previous post on GCNs, we a saw a simple mathematical framework for expressing propagation in GCNs. In short, given an N F⁰ feature matrix X and a matrix representation of the graph structure, e.g., the N N adjacency matrix A of G, each hidden layer in the GCN can be expressed as Hⁱ f(Hⁱ ¹, A)) where H⁰ X and f is a propagation rule. Each layer Hⁱ corresponds to an N Fⁱ feature matrix where each row is a feature representation of a node. These rules computes the feature representation of a node as an aggregate of the feature representations of its neighbors before it is transformed by applying the weights Wⁱ and activation function σ.

Let's first create a model using nothing but PyTorch tensor operations. PyTorch provides methods to create random or zero-filled tensors, which we will use to create our weights and bias for a simple linear model. These are just regular tensors, with one very special addition: we tell PyTorch that they require a gradient. This causes PyTorch to record all of the operations done on the tensor, so that it can calculate the gradient during back-propagation automatically! For the weights, we set requires_grad after the initialization, since we don't want that step included in the gradient.

The human body is frail and people end up in intensive care units for all kinds of reasons. Whatever brings them there, more than half of adults admitted to an ICU end up sharing the same potentially life-threatening condition: kidney damage known as acute kidney injury. The Veterans Administration thinks artificial intelligence could reduce the toll. In a project that drew on roughly 700,000 medical records from US veterans, the agency worked with Google parent Alphabet's DeepMind unit to create software that attempts to predict which patients are likely to develop AKI. The VA hopes to test whether those predictions can help doctors prevent people from developing the condition.

Oracle Linux not only allows you to connect to your data, but also to specialized high-performance hardware to offload deep learning algorithms all in a seamless manner in your own data center or in the cloud.