We are excited to announce the release of TensorFlow v1.8 for ROCm-enabled GPUs, including the Radeon Instinct MI25. This is a major milestone in AMD's ongoing work to accelerate deep learning. ROCm, the Radeon Open Ecosystem, is our open-source software foundation for GPU computing on Linux. AMD provides a pre-built whl package, allowing a simple install akin to the installation of generic TensorFlow for Linux. We've published installation instructions, and also a pre-built Docker image.

We are excited to announce the release of TensorFlow v1.8 for ROCm-enabled GPUs, including the Radeon Instinct MI25. This is a major milestone in AMD's ongoing work to accelerate deep learning. ROCm, the Radeon Open Ecosystem, is our open-source software foundation for GPU computing on Linux. AMD provides a pre-built whl package, allowing a simple install akin to the installation of generic TensorFlow for Linux. We've published installation instructions, and also a pre-built Docker image.

Just enter code fccstevens into the promotional discount code box at checkout at manning.com. In part one, we learned about PyTorch and its component parts, now let's take a closer look and see what it can do. In this article, we explore some of PyTorch's capabilities by playing with pre-trained networks. Computer vision -- a field that deals with making computers to gain high-level understanding from digital images or videos -- is certainly one of the fields most impacted by the advent of deep learning, for a variety of reasons. The need for classifying or interpreting the content of natural images was there, huge datasets became available and new constructs, such as convolutional layers, came about and started to run quickly on GPUs with unprecedented accuracies.

More than 40% of organizations are adding more jobs as a result of bringing artificial intelligence (AI) into their business, according to a Tuesday report from Dun & Bradstreet. Despite worries of AI being a threat to jobs, only 8% of the 100 business executives from Global 2000 organizations surveyed reported that their companies were cutting jobs after implementing the new technology. Some 34% of respondents said job demand was staying the same at their companies regardless of AI implementation. And 18% reported AI not impacting their workforce at all, indicating that the fear around AI may be mostly due to hype. SEE: IT leader's guide to deep learning (Tech Pro Research) AI has a huge presence in the business world, with the majority of organizations using it to some degree, the survey found.

Credit: UCLA Engineering Institute for Technology Advancement Many biosensing applications rely on characterization of specific analytes such as proteins, viruses and bacteria, among many other targets, which can be accomplished by using micro- or nano-scale particles. In such biosensors, these particles are coated with a surface chemistry that makes them stick to the target analyte forming clusters in response. The higher the target analyte concentration is, the larger the number of clusters gets. Therefore, monitoring and characterizing these particle clusters can tell us if the target analyte is present in a sample and in what concentration. Current methods to perform such an analysis are limited in that they are either capable of only a coarse readout or rely on expensive and bulky microscopes, which limit their applicability to address different biosensing needs, especially in resource limited environments.

Topology design optimization offers a tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the topology design problem using a standard topology optimization process, such as SIMP (Simplified Isotropic Material with Penalization) is possible. However, in reality, an estimated over thousands of design iterations is often required for just a few design variables, the conventional optimization approach is, in general, impractical or computationally unachievable for real-world applications significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topology effectively and rapidly. In this study, a novel topology optimization approach based on conditional Wasserstein generative adversarial networks (CWGAN) is developed to replicate the conventional topology optimization algorithms in an extremely computationally inexpensive way. CWGAN consists of a generator and a discriminator, both of which are deep convolutional neural networks (CNN). The limited samples of data, quasi-optimal planar structures, needed for training purposes are generated using the conventional topology optimization algorithms. With CWGANs, the topology optimization conditions can be set to a required value before generating samples.

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture with self-adaptability and high levels of availability and scalability. Integrating DRQN into the adaptation process improves the effectiveness of the adaptation and reduces any adaptation risks, including resources over-provisioning and thrashing. The performance of DRQN is evaluated against deep Q-learning and policy gradient algorithms including: i) deep q-network (DQN), ii) dulling deep Q-network (DDQN), iii) a policy gradient neural network (PGNN), and iv) deep deterministic policy gradient (DDPG). The DRQN implementation in this paper manages to outperform the above mentioned algorithms in terms of total reward, less adaptation time, lower error rates, plus faster convergence and training times. We strongly believe that DRQN is more suitable for driving the adaptation in distributed services-oriented architecture and offers better performance than other dynamic decision-making algorithms. Index Terms Service oriented architecture, self-adaptive architectures, reinforcement learning, Q-learning algorithms, deep Q-Learning networks, recurrent Q-learning networks, policy approximation, multi agents environment. I. INTRODUCTION Self-adaptability refers to the ability of service oriented architecture (SOA) to modify its own structure and behaviour in response to changes in the operating environment [1]. High levels of self-adaptability present the challenges of self-organising, self-tuning, and self-healing the architecture against an interruption. Moreover, because of the services' pervasiveness, and in order to make any adaptation strategy effective and successful, adaptation actions must be considered in conjunction with So that the performed action meets the adaptation goals, objectives, and the desired architecture quality attributes [2]-[4].

In the current field of computer vision, automatically generating texts from given images has been a fully worked technique. Up till now, most works of this area focus on image content describing, namely image-captioning. However, rare researches focus on generating product review texts, which is ubiquitous in the online shopping malls and is crucial for online shopping selection and evaluation. Different from content describing, review texts include more subjective information of customers, which may bring difference to the results. Therefore, we aimed at a new field concerning generating review text from customers based on images together with the ratings of online shopping products, which appear as non-image attributes. We made several adjustments to the existing image-captioning model to fit our task, in which we should also take non-image features into consideration. We also did experiments based on our model and get effective primary results.

In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. We focus on an example of the prediction of acute kidney injury (AKI). Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data. This mechanism is applied in both regression and classification tasks. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets and demonstrated that our proposed model constantly outperforms these baseline models. In particular, the proposed model achieved area under ROC curve at 0.893 on the MIMIC III dataset, and 0.871 on the Philips eICU dataset.

Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data or the validation data. In particular, the fact to overfit the validation data, called "information leak", is almost never treated in papers proposing deep learning models to predict protein-protein interactions (PPI). In this work, we compare two carefully designed deep learning models and show pitfalls to avoid while predicting PPIs through machine learning methods. Our best model predicts accurately more than 78% of human PPI, in very strict conditions both for training and testing. The methodology we propose here allow us to have strong confidences about the ability of a model to scale up on larger datasets. This would allow sharper models when larger datasets would be available, rather than current models prone to information leaks. Our solid methodological foundations shall be applicable to more organisms and whole proteome networks predictions.