Ahead of the ITU Plenipotentiary Conference 2018 (PP-18) – the top policy-making body of the International Telecommunication Union, taking place from 29 October to 16 November in Dubai – ITU News is highlighting some important and emerging areas of ITU's work. The following is an ITU Plenipotentiary Backgrounder, the original can be found on the PP-18 website here. Software has become significantly smarter in recent years. The current expansion of AI is the result of advances in a field known as machine learning. Machine learning involves using algorithms that allow computers to learn on their own by looking through data and performing tasks based on examples, rather than by relying on explicit programming by a human.[1] A machine-learning technique called deep learning, inspired by biological neural networks, finds and remembers patterns in large volumes of data.

Get ready to rev your multimedia engines! The ProLiant ML 350 with the Quadro GV100 is the ideal deep learning solution for providing the extreme compute power necessary for designers, engineers, and others to rapidly create with high visual fidelity. Do you need to create animations and multimedia solutions but need lots of expandability, like a family-sized SUV; security like a Brinks armored truck; and performance like a Bugatti Veyron? You probably already know that the ProLiant ML350 Gen10 Server offers the best performance and expandability in the HPE ProLiant tower portfolio, and it supports two Intel Xeon Scalable processors that give it up to a 71% performance boost [1]. It also delivers up to 3.9x higher virtualized throughput, and more VMs per server2.

Sigmoid: tend to vanish gradient (cause there is a mechanism to reduce the gradient as "a" increases, where "a" is the input of a sigmoid function. When "a" grows to infinite large, S′(a) S(a)(1 S(a)) 1 (1 1) 0.

Sigmoid: tend to vanish gradient (cause there is a mechanism to reduce the gradient as "a" increases, where "a" is the input of a sigmoid function. When "a" grows to infinite large, S′(a) S(a)(1 S(a)) 1 (1 1) 0.

Here you can find 3 Python scripts I'm using to create a classifier for DEEP MACHINE LEARNING and A.I. purposes. The scripts are coded to extract tweets from TWITTER, and create a raw database, than clean the the DB from similar entries, and finally allow you to label the extracted entries, and save the labeled classifier to a new DB. In this example I'm using tweets because I think they are funny, but these scripts can be used with all kind of data.

Gradient boosting decision trees is the state of the art for structured data problems. Two modern algorithms that make gradient boosted tree models are XGBoost and LightGBM. In this article I'll summarize their introductory papers for each algorithm's approach. Gradient Boosting Decision Trees (GBDT) are currently the best techniques for building predictive models from structured data. Unlike models for analyzing images (for that you want to use a deep learning model), structured data problems can be solved very well with a lot of decision trees.

This is a great resource to keep catch the latest news on topics, languages, tools in data science and AI, listen to an industry professional on a podcast, or search for a new job.

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.

Margin enlargement over training data has been an important strategy since perceptrons in machine learning for the purpose of boosting the robustness of classifiers toward a good generalization ability. Yet Breiman shows a dilemma (Breiman, 1999) that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization errors. In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed spectrally normalized margins. A novel perspective is provided to explain Breiman's dilemma based on phase transitions in dynamics of normalized margin distributions, that reflects the trade-off between expressive power of models and complexity of data. When data complexity is comparable to the model expressiveness in the sense that both training and test data share similar phase transitions in normalized margin dynamics, two efficient ways are derived to predict the trend of generalization or test error via classic margin-based generalization bounds with restricted Rademacher complexities. On the other hand, over-expressive models that exhibit uniform improvements on training margins, as a distinct phase transition to test margin dynamics, may lose such a prediction power and fail to prevent the overfitting. Experiments are conducted to show the validity of the proposed method with some basic convolutional networks, AlexNet, VGG-16, and ResNet-18, on several datasets including Cifar10/100 and mini-ImageNet.

To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probability distribution of the softmax layer in a teacher network and thus neglect the intermediate representations. In this paper, we propose a knowledge transfer adversarial network to better train a student network. Our technique holistically considers both intermediate representations and probability distributions of a teacher network. To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained. Specifically, we arrange a Teacher-to-Student layer for enabling our framework suitable for various student structures. The intermediate representation helps the student network better understand the transferred generalization as compared to the probability distribution only. Furthermore, we infuse an adversarial learning process by employing a discriminator network, which can fully exploit the spatial correlation of feature maps in training a student network. The experimental results demonstrate that the proposed method can significantly improve the performance of a student network on both image classification and object detection tasks.