Abstract--Recent papers in neural machine translation have proposed the strict use of attention mechanisms over previous standards such as recurrent and convolutional neural networks (RNNs and CNNs). We propose that by running traditionally stacked encoding branches from encoder-decoder attentionfocused architectures in parallel, that even more sequential operations can be removed from the model and thereby decrease training time. In particular, we modify the recently published attention-based architecture called Transformer by Google, by replacing sequential attention modules with parallel ones, reducing the amount of training time and substantially improving BLEU scores at the same time. Experiments over the English to German and English to French translation tasks show that our model establishes a new state of the art. Historically, statistical machine translation involved extensive work in the alignment of words and phrases developed by linguistic experts working with computer scientists [1].

A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model -- its responses as well as failures -- more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.

In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be %necessarily and minimally and sufficiently present (viz. important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz. certain background pixels). We argue that such explanations are natural for humans and are used commonly in domains such as health care and criminology. What is minimally but critically \emph{absent} is an important part of an explanation, which to the best of our knowledge, has not been explicitly identified by current explanation methods that explain predictions of neural networks. We validate our approach on three real datasets obtained from diverse domains; namely, a handwritten digits dataset MNIST, a large procurement fraud dataset and a brain activity strength dataset. In all three cases, we witness the power of our approach in generating precise explanations that are also easy for human experts to understand and evaluate.

The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it's important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about. First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What's worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors. Fortunately, Machine Learning can aid in solving the most common tasks including regression, prediction, and classification.

Deep learning neural networks are capable of automatically learning and extracting features from raw data. This feature of neural networks can be used for time series forecasting problems, where models can be developed directly on the raw observations without the direct need to scale the data using normalization and standardization or to make the data stationary by differencing. Impressively, simple deep learning neural network models are capable of making skillful forecasts as compared to naive models and tuned SARIMA models on univariate time series forecasting problems that have both trend and seasonal components with no pre-processing. In this tutorial, you will discover how to develop a suite of deep learning models for univariate time series forecasting. How to Develop Deep Learning Models for Univariate Time Series Forecasting Photo by Nathaniel McQueen, some rights reserved. You can learn more about the dataset from DataMarket. Save the file with the filename'monthly-car-sales.csv' in your current working directory. We can load this dataset as a Pandas series using the function read_csv(). Once loaded, we can summarize the shape of the dataset in order to determine the number of observations. We can then create a line plot of the series to get an idea of the structure of the series. We can tie all of this together; the complete example is listed below.

Something that I have been wanting to play around with is generating text to describe images. When posed in this way two pathways come to mind. First would be to use a combination of CNNs for feature extraction and feed those extracted features to an LSTM and let it generate descriptions by iterating repeatedly. The second way would be to build a multi-label classification model and have the output nodes represent specific tags. The first model is good in the case you wanted to generate captions for images that have a grammatical structure to them.

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC. To make sure you keep getting these emails, please add [email protected] to your address book or whitelist us.

Come join us as we dive into how we're applying deep learning across Twitter and solving some of the challenges our engineers face. In order to attend you must RSVP on the registration link below. AGENDA: 6pm Doors Open 6:30pm Tech Talks Begin 7:15pm Q&A 7:30 - 8pm Networking TOPICS INCLUDE: CHALLENGES IN RECOMMENDER SYSTEMS - ASHISH BANSAL "Twitter has amazing and unique content that is generated at an enormous velocity internationally. A constant challenge is how to find the relevant content for users so that they can engage in the conversation. Approaches span collaborative filtering and content based recommendation systems for different use cases. This talk gives insight into unique recommendation system challenges at Twitter's scale and what makes this a fun and challenging task."

In this article, we will see the implementation of Convolutional Neural Network (CNN) using Keras on MNIST data set and then we will compare the results with the regular neural network. It is highly recommended to first read the post "Convolutional Neural Network -- In a Nutshell" before moving on to CNN implementation to develop intuition about CNN. The MNIST dataset is most commonly used for the study of image classification. The MNIST database contains images of handwritten digits from 0 to 9 by American Census Bureau employees and American high school students. It is divided into 60,000 training images and 10,000 testing images.

The Scripps Research Translational Institute is partnering with graphics firm NVIDIA to develop AI and deep learning best practices, tools and infrastructure to develop AI applications using genomic and digital health sensor data. With NVIDIA, California-based research organisation Scripps will establish a centre of excellence for artificial intelligence in genomics and digital sensors. Scripps and NVIDIA will work to advance the use of machine learning and deep learning to harness the exploding quantity of health data. The partnership will focus on data generated by faster, more affordable genome sequencing gear, and digital health sensors such as smartwatches, blood pressure cuffs and glucose monitors. NVIDIA AI experts and Scripps researchers and clinicians will use deep learning and machine learning, to tackle the deluge of genomics and sensor data.