Cloud computing will soon be a norm for hosting software applications catering to a variety of use-cases in different verticals. Cloud Computing refers to Internet-based services that provide access to managed IT resources; these resources are managed by experts and are available on-demand on a pay-per-use model. This enables the application developers to focus on the use-case and come up with an MVP (minimum viable product) in a shorter period.

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the efficacy of the proposed technique.

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.

Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

Over the past few years, surging focus on neuroscience and prospect of understanding brain functionality have assisted in addressing current technological limitations by utilising neural computation principles. Recognising this potentiality, the research community has launched many remarkable projects to support computational neuroscience, for studying the nervous system's information processing properties. An example of this is the Blue Brain Project to be held in Switzerland at Ecole Polytechnique Federale de Lausanne. This project focuses on simulation of ten thousand neurons in rat's brain by analysing the nervous system in detail. The capability of neural network models to solve several tasks was realised in early 2000's, on the basis of human brain's working process. "Deep learning" has become a buzzphrase and a prevalent term for every neural network model as well as associated technique.

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data.

Covers how the book is structured. The central theme of this book is that by understanding the many patterns and their inter-relationships we find in Deep Learning practice we begin to understand how we can best compose solutions. On Pattern Languages - Pattern Languages are languages derived from entities called patterns that when combined form solutions to complex problems. Each pattern describes a problem and offers solutions. Pattern languages are a way of expressing complex solutions that were derived from experience such that others can gain a better understanding of the solution.

This is the code for the article'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software. The field took off last year when Tony Beltramelli introduced the pix2code paper and Airbnb launched sketching interfaces. Currently, the largest barrier to automating front-end development is computing power.

Artificial intelligence (AI) is fast becoming one of the most important areas of digital expansion in history. The CEO of Applied Materials recently stated that "the war" for AI leadership will be the "biggest battle of our lifetime."1 AI promises to transform almost every industry, including healthcare (diagnosis, treatments), automotive (autonomous driving), manufacturing (robot assembly), and retail (purchasing assistance). Although the field of AI has been around since the 1950s, it was not until very recently that computing power and the methods used in AI have reached a tipping point for major disruption and rapid advancement. Both of these areas have a tremendous need for much higher memory bandwidth.