Mention machine learning, and many common frameworks pop into mind, from "old" stalwarts like Scikit-learn to juggernauts like Google's TensorFlow. But the field is large and diverse, and useful innovations are bubbling up across the landscape. Recent releases from three open source projects continue the march toward making machine learning faster, more scalable, and easier to use. PyTorch and Apache MXNet bring GPU support to machine learning and deep learning in Python. Smile promises speed, convenience, and a comprehensive library of machine learning algorithms to Java developers.

Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the following challenge: can a nontrivial AI model reproduce natural chemical diversity for desired molecules? To illustrate this question, we consider two generative models: a Reinforcement Learning model and the recently introduced ORGAN. Both fail at this challenge. We hope this challenge will stimulate research in this direction.

There is a spoiler warning later in this post.* Minutes after the epic finale of the seventh season of Game of Thrones, fans of the show were already dismayed to hear that the final, six-episode season of the series isn't set to air until spring 2019. For readers of the A Song of Ice and Fire novel series on which the TV show is based, disappointment stemming from that estimated wait time is laughable. The fifth novel in seven-novel series, A Dance with Dragons, was published in 2011 and author George R.R. Martin has been laboring over the The Winds of Winter since, with no release date in sight. With no new source material, producers of the TV series have been forced to move the story forward themselves since late season 6. Tired of the wait and armed with technology far beyond the grand maesters of Oldtown, full-stack software engineer Zack Thoutt is training a recurrent neural network (RNN) to predict the events of the unfinished sixth novel.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

It's late summer, which means three things: pennant races heat up in baseball, kids go back to school, and Gartner releases its annual Hype Cycle for Emerging Technologies. Gartner identified AI Everywhere as one of three "megatrends" for the 2017 report (the others are transparently immersive experiences and digital platforms). The hype surrounding AI and related technologies has certainly perked up in 2017, as the IT and analytics industry appears to have picked up its considerable luggage related to big data analytics and deposited it firmly on the AI train. With AI now broadly carrying the torch for big data analytics, it's no surprise that we're seeing continued excitement around related technologies, particularly machine learning and deep learning. "AI technologies will be the most disruptive class of technologies over the next 10 years due to radical computational power, near-endless amounts of data and unprecedented advances in deep neural networks," says Mike J. Walker, research director for Gartner.

Unseen data conditions can inflict serious performance degradation on systems relying on supervised machine learning algorithms. Because data can often be unseen, and because traditional machine learning algorithms are trained in a supervised manner, unsupervised adaptation techniques must be used to adapt the model to the unseen data conditions. However, unsupervised adaptation is often challenging, as one must generate some hypothesis given a model and then use that hypothesis to bootstrap the model to the unseen data conditions. Unfortunately, reliability of such hypotheses is often poor, given the mismatch between the training and testing datasets. In such cases, a model hypothesis confidence measure enables performing data selection for the model adaptation. Underlying this approach is the fact that for unseen data conditions, data variability is introduced to the model, which the model propagates to its output decision, impacting decision reliability. In a fully connected network, this data variability is propagated as distortions from one layer to the next. This work aims to estimate the propagation of such distortion in the form of network activation entropy, which is measured over a short- time running window on the activation from each neuron of a given hidden layer, and these measurements are then used to compute summary entropy. This work demonstrates that such an entropy measure can help to select data for unsupervised model adaptation, resulting in performance gains in speech recognition tasks. Results from standard benchmark speech recognition tasks show that the proposed approach can alleviate the performance degradation experienced under unseen data conditions by iteratively adapting the model to the unseen datas acoustic condition.

We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by replacing the first few layers of a CNN network with a parametric log based DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations that are used by the later layers of the CNN to learn high-level features. This improves the training of the network as the later layers can learn more complex patterns from the start of learning because the edge representations are already present. The efficient learning of the DTSCNN network is demonstrated on CIFAR-10 and Caltech-101 datasets. The generic nature of the ScatterNet front-end is shown by an equivalent performance to pre-trained CNN front-ends. A comparison with the state-of-the-art on CIFAR-10 and Caltech-101 datasets is also presented.

Try this Deep Learning App yourself (refresh a couple of times initially if there's Application Error): Dot 0: Deep Learning in Sentiment Analysis Sentiment analysis is a powerful application which extends its arms to the following fields in the modern day world. According to Wikipedia: Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Currently, it's creating waves in the field of Image processing, Natural Language Processing, Speech Processing, Video Processing etc. Movie Review Sentiment App uses a customized data-set of numerous movie reviews from various different sources like IMDB, UCI, Cornell dataset etc The process of making a gold data-set(Prepared data-set for prediction) is a step by step process of Data Collection, Data Cleaning, Data Normalization after which the data can be termed as a gold data-set for the prediction process.

Throughout the last few years in machine learning, I've always wanted to build real machine learning products. A few months ago, after taking the great Fast.AI deep learning course, it seemed like the stars aligned, and I have the opportunity: The advances in deep learning technology permitted doing many things that weren't possible before, and new tools were developed and made the deployment process more accessible than ever. In the aforementioned course, I've met Alon Burg, who is an experienced web developer, an we've partnered up to pursue this goal. Together, we've set ourselves the following goals: Our early thoughts were to take on some medical project, since this field is very close to our hearts, and we felt (and still feel) that there is an enormous number of low hanging fruits for deep learning in the medical field. However, we realized that we are going to stumble upon issues with data collection and perhaps legality and regulation, which was a contradiction with our will to keep it simple.

After we explored our first option of running a deep learning model on the server side, let's consider running it in the browser. We will be using a default template to bootstrap our app, and tweak it a little to support KerasJS. Now that we're ready to start development, run npm run dev to start your local dev server. A new browser tab will open showing your app. Webpack Hot Module Reload (HMR) will automatically refresh the browser each time you save a file to reflect the changes.