We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as the representation space becomes less entangled. We explore the structure of the representation space by interpolating between sentences, which yields interesting pseudo-English sentences, many of which have recognizable syntactic structure. Lastly, we point out an interesting property of our models: The difference-vector between two sentences can be added to change a third sentence with similar features in a meaningful way.

Staying on top of recent work is an important part of being a good researcher, but this can be quite difficult. Thousands of new papers are published every year at the main ML and NLP conferences, not to mention all the specialised workshops and everything that shows up on ArXiv. Going through all of them, even just to find the papers that you want to read in more depth, can be very time-consuming. In this post, I have summarised 50 papers. After going through a paper, if I had the chance, I would write down a few notes and summarise the work in a couple of sentences. These are not meant as reviews – I'm not commenting on whether I think the paper is good or not. But I do try to present the crux of the paper as bluntly as possible, without unnecessary sales tactics. Hopefully this can give you the general idea of 50 papers, in roughly 20 minutes of reading time. The papers are not selected or ordered based on any criteria. It is not a list of the best papers I have read, more like a random sample.

Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found here. The repo has been modified to run both ES and our algorithms, including our Deep Genetic Algorithm (DeepGA) locally and on AWS. Note: The Humanoid experiment depends on Mujoco. If you plan to use the mujoco env, make sure to follow mujoco-py's readme about how to install mujoco correctly The extra folder holds the XML specification file for the Humanoid Locomotion with Deceptive Trap domain used in https://arxiv.org/abs/1712.06560.

The application of big data is enabling us to reveal deeper marketing insights all the time. Whether it's open data revealing predictive patterns in consumer behaviour or hyper-personalisation creating a bespoke online shopping experience, big data in action is revolutionising the way we gather and act on business intelligence. This in-depth data insight is made possible by machines; clever programs and algorithms that can process data at speeds no human could match, and derive meaning it would take us a lot longer to uncover. This is the power of artificial intelligence (AI), machine learning and deep learning; three industry buzzwords continuing to make a huge impact in the world of data marketing. We've already taken a look at AI and how companies are using it to improve their services, and in this blog we're delving into deep learning.

Somewhere, buried in one of tens of millions of cell samples, could lie the next great breakthrough in disease prevention or cure. But one of the great barriers to finding it could be the need for human eyes to evaluate a corresponding mountain of cell images, one by one. In an era when terabytes of data can be analyzed in just a few days, the opportunity to enhance automation of biomedical analysis could help researchers achieve breakthroughs faster in the treatment of almost every disease--from cancer, diabetes and rare disorders to the common cold. To spur this automation, Booz Allen Hamilton (NYSE: BAH) and Kaggle today launched the 2018 Data Science Bowl, a 90-day competition that calls on thousands of participants globally to train deep learning models to examine images of cells and identify nuclei, regardless of the experimental setup--and without human intervention. Creators of the top algorithms will split $170,000 in cash and prizes, including an NVIDIA DGX Station, a personal AI supercomputer that delivers the computing capacity of 400 CPUs in a desktop workstation.

This'Top 10' list has been created on the basis of best content, and not exactly the number of views. To help you choose an appropriate framework, we first start with a video that compares few of the popular Python DL libraries. I have included the highlights and my views on the pros and cons of each of these 10 items, so you can choose one that best suits your needs. I have saved the best for last- the most comprehensive yet free YouTube course on DL . Before I actually list the best DL in Python videos, it is important that one understands the differences between the 5 most popular deep learning frameworks -SciKit Learn, TensorFlow, Theano, Keras, and Caffe.

Google just made it a lot easier to build your very own custom AI system. A new service, called Cloud AutoML, uses several machine-learning tricks to automatically build and train a deep-learning algorithm that can recognize things in images. The technology is limited for now, but it could be the start of something big. Building and optimizing a deep neural network algorithm normally requires a detailed understanding of the underlying math and code, as well as extensive practice tweaking the parameters of algorithms to get things just right. The difficulty of developing AI systems has created a race to recruit talent, and it means that only big companies with deep pockets can usually afford to build their own bespoke AI algorithms.

Despite the promising results obtained with [representations developed from Web image], the experiments demonstrate that object classification with real-life robotic data is far from being solved."

Whit Andrews, vice president and distinguished analyst at Gartner, was able to put some hard numbers to the trend. "We are in the very earliest stages of enterprise adoption of artificial intelligence," he said. "Specifically, in our most recent CIO survey from 2017, one in 25 CIOs described themselves as having artificial intelligence in action in their organizations." The companies farthest along with the technology tend to be technology giants, said Hadley Reynolds, managing director and co-founder of the Cognitive Computing Consortium. These companies are "basing much of their businesses on various kinds of machine learning and deep learning technologies," he said, so they have invested heavily in research and recruiting talent with AI skills.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios. I will then provide examples of applications of transfer learning before delving into practical methods that can be used to transfer knowledge.