This past year, I've seen a lot of repos for implementations that train *2vec, and other embeddings. In order to increase interest, is it possible to add into your repo either real-life or manufactured examples (or even links to papers / research) where people use said vector embeddings and actually do something with them?

One oft-cited solution to the big data challenge of digital mental health data is to use artificial intelligence approaches like deep learning to help make sense of the raw data. Deep learning is the art and science of building enormous computer models--neural networks--that can be used to predict, classify, edit, describe, and create videos, images, and text. For example, apps can organize your photos by topic, even if you never labeled them yourself. But analyzing and helping human beings is much more difficult than helping a machine recognize the difference between a selfie and a landscape photo. Artificial intelligence programs still struggle with cancer diagnoses, even when complete medical records are available and even with medical knowledge of that cancer well characterized at the genetic level.

Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.

I never really had any issues installing required stuff with the Amazon Linux AMI. The most annoying parts were always having to download CuDNN from Nvidia and compiling the current master branches for Tensorflow and Keras, which I'd most likely have to do here as well. Though, I'm sure this will be a big help for some people who aren't strong with Linux.

Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. In this blog, based on Flutura's extensive experience in the oil and gas industry, we have highlighted three practical use cases, from the trenches, where these technologies are practically applied to solve real-life problems and impact meaningful business outcomes. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications. You forgot to provide an Email Address.

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ago, Google released a new object detection API for Tensorflow.

I have been a collector of books and classes of machine learning and deep learning for the last few years. Even though I come from a strong theoretical background, I have to say one must do hands on tinkering to be able to solve one's own problem successfully. Then for deep learning one must work with Tensorflow or Theano. However, I have been searching for a good hands-on book on tensorflow and had found none until this book. I purchased the kindle version so I can dive into this book early before the book comes out.

AI that can understand how you're feeling based on the emotion in your voice will open up whole new areas of personalization. You know when people say that it's not what you say, but how you say it that matters? Well, very soon that could become a part of smart assistants such as Amazon's Alexa or Apple's Siri. At least, it could if these companies decide to use new technology developed by emotion tracking artificial intelligence company Affectiva. Affectiva's work has previously focused on identifying emotion in images by observing the way that a person's face changes when they express particular sentiments.

In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to improve the speed of convergence of a minimisation method for deep learning. I explored the family of gradient descent methods - even though not exhaustively - giving a list of approaches that deep learning researchers are considering for different scenarios. Every method has its own benefits and drawbacks, pretty much depending on the type of data, and data sparsity. But there is one method that seems to be, at least empirically, the best approach so far. Feel free to listen to the previous episode, share it, re-broadcast or just download for your commute.

Keeping up with technology trends can be exhausting and confusing. Many times, the terms used to describe technology investing opportunities aren't always defined and can leave investors with more questions than answers. So let's take a quick look at how NVIDIA Corporation (NASDAQ:NVDA), a graphics process maker with a leadership position in these spaces, defines each of them -- and what the company's potential is in these businesses. Artificial intelligence (AI) is sometimes thought of as the intelligence we see from robots in movies or television shows. That level of AI isn't possible yet, and instead, tech companies that are working on artificial intelligence right now are usually doing what's called "narrow AI."