Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities, but also a rise in new development frameworks and methodologies, most of which are backed by open source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open source machine learning frameworks -- what they offer, and what use cases they can best be applied to.

Editor's Note: Download this Free eBook: Getting Started with Apache Spark 2.x – from Inception to Production In this blog post, we will give an introduction to machine learning and deep learning, and we will go over the main Spark machine learning algorithms and techniques with some real-world use cases. The goal is to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and data scientists work with domain experts, architects, developers, and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This overview is meant to provoke a review of your own data to identify new opportunities.

The insane pre-holiday shopping is behind us, along with celebrations, and personal to-do lists for the next 12 months. So, let's analyze the data science and artificial intelligence accomplishments and events of the past year. We talked with experts from Booking.com, Wolfram Research, BetConstruct, and other data science specialists who shared their thoughts about opportunities as well as their influence on business, research, and everyday lives for both industries. Experts have different points of view on whether 2018 was rich in important achievements and events. No recent achievements can compete with inventions of a multilayer perceptron (MLP), neural net training techniques like backpropagation and backpropagation through time (BPTT), residual networks, the introduction of Generative Adversarial Networks (GANs), and deep Q-learning networks (DQN). "So, looking back to memorable ones I listed before, there weren't'brand new' accomplishments in 2018," summarizes Oleksandr.

Indirectly, the efficiencies and opportunities that deep learning and automation enable for businesses have the potential to create millions of jobs. While automated delivery methods such as self-driving delivery trucks will take thousands of drivers off the road, a recent Strategy Business article suggests that, "In a world where companies are increasingly judged on the quality of the customer experience they provide, you will need employees who can combine the skills of a customer service agent, marketer, and salesperson to sit in those trucks and engage with customers as they make deliveries." Additionally, the higher productivity and positive growth enabled by AI will have a positive effect on hiring as companies will simply need to hire more workers to take on existing tasks that require very human skills. Think of customer service, copywriters, program managers, and other roles that require skills such as empathy, ethical judgement, and creativity.

Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings -- one per amino acid position -- that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrarylength sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Proteins are linear chains of amino acid residues that fold into specific 3D conformations as a result of the physical properties of the amino acid sequence. These structures, in turn, determine the wide array of protein functions, from binding specificity to catalytic activity to localization within the cell.

Hindi writing often Given a user input in the form of an ink, i.e. a list of contains a connecting'Shirorekha' line and characters touch or pen strokes, output the textual interpretation can form larger structures (grapheme clusters) which of this input. A stroke is a sequence of points (x, y, t) influence the written shape of the components. Arabic with position (x, y) and timestamp t. is written right-to-left (with embedded left-to-right sequences Figure 1 illustrates example inputs to our online used for numbers or English names) and characters handwriting recognition system in different languages change shape depending on their position within and scripts. The left column shows examples in English a word. Emoji are non-text Unicode symbols that we with different writing styles, with different types also recognize. of content, and that may be written on one or multiple lines. The center column shows examples from Online handwriting recognition has recently been five different alphabetic languages similar in structure gaining importance for multiple reasons: (a) An increasing to English: German, Russian, Vietnamese, Greek, and number of people in emerging markets are obtaining Georgian. The right column shows scripts that are significantly access to computing devices, many exclusively using different from English: Chinese has a much mobile devices with touchscreens. Many of these users larger set of more complex characters, and users often have native languages and scripts that are not as easily overlap characters with one another. Korean, while an typed as English, e.g.

Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application contexts require adapting to diverse behaviors. In this paper we examine how to build a model that can make use of different temporal ranges and dynamics depending on the request context. We begin with the analysis of an anonymized Youtube dataset comprising millions of user sequences. We quantify the degree of long-range dependence in these sequences and demonstrate that both short-term and long-term dependent behavioral patterns co-exist. We then propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution to deal with both short-term and long-term dependencies. Our approach employs a mixture of models, each with a different temporal range. These models are combined by a learned gating mechanism capable of exerting different model combinations given different contextual information. In empirical evaluations on a public dataset and our own anonymized YouTube dataset, M3 consistently outperforms state-of-the-art sequential recommendation methods.

Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown or recognize familiar objects. Its active nature is impressively evident in humans which from early on reliably acquire sophisticated sensory-motor capabilites for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. In stark contrast, in robotics the relative lack of good real-world interaction models, along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods has so far rendered haptic exploration a largely underdeveloped skill for robots, very unlike vision where deep learning approaches and an abundance of available training data have triggered huge advances. In the present work, we connect recent advances in recurrent models of visual attention (RAM) with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel learning architecture that learns a generative model of haptic exploration in a simplified three-dimensional environment. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. The resulting method has been successfully tested with four different objects. It achieved results close to 100% while performing object contour exploration that has been optimized for its own sensor morphology.

Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recent work on explaining deep models has introduced approaches to provide insights toward the model's behavior and predictions, which are helpful for determining the reliability of the model's prediction. However, such methods do not fix and improve the model's reliability. In this paper, we teach our models to make the right prediction for the right reason by providing explanation training signal and ensuring alignment of the models explanation with the ground truth explanation. Our experimental results on multiple tasks and datasets demonstrate the effectiveness of the proposed method, which produces more reliable predictions while delivering better results compared to traditionally trained models.