Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

We are excited to announce the AWS Machine Learning Research Awards, a new program that funds university departments, faculty, PhD students, and post-docs that are conducting novel research in machine learning (ML). We are working with Carnegie Mellon University, California Institute of Technology (Caltech), Harvard Medical School, The University of Washington, and the University of California, Berkeley on this program. The scale and performance of the AWS Cloud, coupled with powerful frameworks like Apache MXNet, TensorFlow, Caffe2, Microsoft Cognitive Toolkit (CNTK), and PyTorch, allow an unprecedented opportunity to drive machine learning research forward. The goal of this program is to help researchers accelerate the development of innovative algorithms, publications, and source code across a wide variety of machine learning applications and focus areas. In addition to funding, award recipients receive computing resources, training, mentorship from Amazon scientists and engineers, and have the opportunity to attend a research seminar at the AWS headquarters in Seattle.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years. You will start with exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you will dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you will learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. The elements of deep learning neural networks, types of deep learning networks, frameworks used for deep learning applications will be addressed and applications will be done with R TensorFlow package. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

The deep learning landscape is constantly changing. Theano was the first widely adopted deep learning framework, created and maintained by MILA-- headed by Yoshua Bengio, one of the pioneers of deep learning. In September of this year MILA announced that there will be no further development work on Theano in 2018 after releasing the latest version. The news didn't come as a surprise. In the past years different open source Python deep learning frameworks were introduced, often developed or backed by one of the big tech companies, and some got a lot of traction.

I am currently of the opinion that unbounded receptive field of RNNs is often a curse, have tried many models where hard truncation (resetting the memory) at a fixed or even random interval was important to get it to actually work in generation. I think a lot of what people care about in generative models are more like "medium term" dependencies (more exactly, do true "long term dependencies" exist? At least one case in particular here is burned in my brain forever. Hierarchies are often useful, whether you get it from multiple RNNs and skip connections, HM-RNN, SampleRNN, fixed interval hidden passing from a fast RNN to "slow" one, WaveNet style dilated convolutions, or in more roundabout ways using trees, memory, stacks, etc. One really interesting part of these convolutional generative models was pointed out to me by Laurent Dinh, I mention it in this review of PixelRNN/CNN - growing the dependency chain over depth makes tons of sense for a lot of problems, and is a general idea that is useful in a ton of domains.

If there is anything that chip giant Intel has learned over the past two decades as it has gradually climbed to dominance in processing in the datacenter, it is ironically that one size most definitely does not fit all. As the tight co-design of hardware and software continues in all parts of the IT industry, we can expect fine-grained customization for very precise – and lucrative – workloads, like data analytics and machine learning, just to name two of the hottest areas today. Software will run most efficiently on hardware that is tuned for it, although we are used to thinking of that process in a mirror image, where programmers tweak their code to take advantage of the forward-looking features a chip maker conceives of four or five years before they are etched into its transistors and delivered as a product. The competition is fierce these days, and Intel has to move fast if it is to keep its compute hegemony in the datacenter. That is why at the Intel Developer Forum in San Francisco the company put a new path on the Knights family of many-core processors that will see the company deliver a version of this chip specifically tuned for machine learning workloads.

More and more articles are appearing on Artificial Intelligence (AI, machine learning, (or deep learning), and many writers talk about AI, machine learning and data science without differentiation, as if these terms were broadly interchangeable. Let us start by describing Artificial Intelligence as the implementation of intelligent agents. According to Peter Norvig and Stuart Russel, an intelligent agent is an autonomous entity capable of perceiving its environment via sensors, of interacting with it using actuators (in other words, interacting with its environment), capable of learning, analysing, using knowledge, and taking decisions. Historically, the first AIs were not actually "learning". At best they used heuristic functions combined with rules engines.

Christmas carol songwriters should be relieved to hear that they can keep their jobs for a little while longer. It turns out that artificial intelligence hasn't quite mastered the art of their job. In a Dec. 21 entry on her personal AI blog, Janelle Shane, a research scientist in industry and machine-learning hobbyist in her spare time, chronicles her journey of trying to teach a neural network to generate Christmas lyrics. I trained a neural network to write Christmas carols and it got confused. In retrospect I should have seen this coming.

Artificial intelligence is hot, and you can tell that because both giant companies and tiny startups are excited about it. Nvidia, which had $6.9 billion in revenues last year, is in touch with more than 2,000 AI startups around the world. And this week, the graphics chip maker and AI company took a step in figuring out which ones are the best. Jen-Hsun Huang, CEO of Nvidia, hosted a Shark Tank style event called Nvidia Inception to find the best AI startups. Huang and a panel of judges listened to pitches from 14 AI startups across three categories.