I've been playing with several GAN implementations found on github, and the authors often claim the example is under- or overfitting. How is this assessed / how do they know since there is no "validation" set.

Bengaluru: Hewlett Packard Enterprise (HPE) today announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. To help customers overcome these challenges and realize the potential of AI, HPE is announcing the following offerings: • HPE's Rapid Software Development for AI: HPE introduced an integrated hardware and software solution, purpose-built for high performance computing and deep learning applications.

A Reddit study group for the free online version of the Stanford class "Machine Learning", taught by Andrew Ng. The purpose of this reddit is to help each other understand the course materials, not to share solutions to assignments. Please follow the Stanford Honor Code. I'm a new user to Reddit, how does this site work? I have a question about the (class / videos / quiz / homework), how can I get help?

Cher Horowitz's closet from the film "Clueless" had a futuristic computer system that helped her put together outfits. Back in 1995, the concept teased what it might be like to get dressed in the future. Technology has evolved a lot since then, but closets have been largely untouched by innovation. Now, that's starting to change. "If algorithms do their job well, people will spend less time thinking about what to wear," said Ranjitha Kumar, an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

They're probably the person in your life you go to help for all your technology needs. So how can you give something good to the tech-savvy person in your life? Here are some suggestions for gifts to delight those who are always looking at the hottest tech trends and products. As with all cutting-edge tech, this isn't for the faint of heart, both in terms of price and in willingness to try something new. You still won't find a headphone jack with the The iPhone X.

If you're reading this article, chances are that you're pretty aware of the fact that many things are moving from the physical world to the digital one -- from editorial content to retail. But sometimes, those online experiences still leave something to be desired; a trend we've noticed over the past couple of years is retailers harnessing technology in an attempt to mimic the level of customer service and personalization you might get from a really good, attentive salesperson IRL. While shopping online is supposed to be convenient, it can often be overwhelming. Many online retailers boast tens or hundreds of thousands of brands and SKUs, and if you don't know exactly what you're looking for, and how it will fit you, the experience can be pretty frustrating. To mitigate that, retailers with the resources to do so are working to use data collection and, in some cases, AI to create more personalized shopping experiences -- i.e., showing you products it thinks you will like based on what you've purchased before, sort of like fashion's version of Spotify Discover Weekly or Apple Music's For You tab.

I have this portfolio with jupyter notebooks done by me. Several of them need to be reworked or deleted, but most of them are okay. One of them is similar to things which I did while I worked in a bank. As for the first project - this is my attempt to build a site with handwritten digit recognition system with online training. This portfolio really helped me when I was looking for a job.

The most futuristic thing I have ever bought used to be a Sonos music player. I'd have people over just to show it off. "Name a song," I'd say. "Go on, any version of any song by any act that ever lived. So they would, and I'd pull out my phone and – hey presto – seconds later, that song would boom out across my living room like magic. When friends moved house, I'd see their stupid boxes of old CDs and laugh. "You antiquated morons," I'd gloat. "When I move house, I'll be able to fit every song ever recorded into a shoebox.

The variational autoencoder (VAE) is a popular probabilistic generative model. However, one shortcoming of VAEs is that the latent variables cannot be discrete, which makes it difficult to generate data from different modes of a distribution. Here, we propose an extension of the VAE framework that incorporates a classifier to infer the discrete class of the modeled data. To model sequential data, we can combine our Classifying VAE with a recurrent neural network such as an LSTM. We apply this model to algorithmic music generation, where our model learns to generate musical sequences in different keys. Most previous work in this area avoids modeling key by transposing data into only one or two keys, as opposed to the 10+ different keys in the original music. We show that our Classifying VAE and Classifying VAE+LSTM models outperform the corresponding non-classifying models in generating musical samples that stay in key. This benefit is especially apparent when trained on untransposed music data in the original keys.