NVIDIA's meteoric growth in the datacenter, where its business is now generating some $1.6B annually, has been largely driven by the demand to train deep neural networks for Machine Learning (ML) and Artificial Intelligence (AI)--an area where the computational requirements are simply mindboggling. Much of this business is coming from the largest datacenters in the US, including Amazon, Google, Facebook, IBM, and Microsoft. Recently, NVIDIA announced new technology and customer initiatives at its annual Beijing GTC event to help drive revenue in the inference market for Machine Learning, as well as solidify the company's position in the huge Chinese AI market. For those unfamiliar, inference is where the trained neural network is used to predict and classify sample data. It is likely that the inference market will eventually be larger, in terms of chip unit volumes, than the training market; after all, once you train a neural network, you probably intend to use it and use it a lot.

Today AMAX.AI launched the [SMART]Rack AI Machine Learning cluster, an all-inclusive rackscale platform is maximized for performance featuring up to 96x NVIDIA Tesla P40, P100 or V100 GPU cards, providing well over 1 PetaFLOP of compute power per rack. The [SMART]Rack AI is revolutionary to Deep Learning data centers," said Dr. Rene Meyer, VP of Technology, AMAX. "Because it not only provides the most powerful application-based computing power, but it expedites DL model training cycles by improving efficiency and manageability through integrated management, network, battery and cooling all in one enclosure." The solution is fully-loaded with features designed to accelerate compute as well as data-transfer performance while offering the ultimate in manageability. Solution components include: an All-Flash storage appliance for an ultra-fast in-rack data repository; 25G high-speed network; [SMART]DC HPC-optimized DCIM to remotely monitor, manage and orchestrate GPU-based Machine Learning hardware where real-time temperature, power and system health monitoring are critical for uninterrupted operation; and an in-rack battery for graceful shutdowns in the event of a power loss scenario.

"How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile devices to reducing the energy bill of a datacenter, it is important to understand the energy efficiency of CNNs during serving for making an inference, before actually training the model. In this work, we propose NeuralPower: a layer-wise predictive framework based on sparse polynomial regression, for predicting the serving energy consumption of a CNN deployed on any GPU platform. Given the architecture of a CNN, NeuralPower provides an accurate prediction and breakdown for power and runtime across all layers in the whole network, helping machine learners quickly identify the power, runtime, or energy bottlenecks. We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy. The experimental results show that the prediction accuracy of the proposed NeuralPower outperforms the best published model to date, yielding an improvement in accuracy of up to 68.5%. We also assess the accuracy of predictions at the network level, by predicting the runtime, power, and energy of state-of-the-art CNN architectures, achieving an average accuracy of 88.24% in runtime, 88.34% in power, and 97.21% in energy. We comprehensively corroborate the effectiveness of NeuralPower as a powerful framework for machine learners by testing it on different GPU platforms and Deep Learning software tools.

In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent; (iv) as opposed to some classical machine learning models, oversampling does not necessarily cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

Detect facial keypoints is a critical element in face recognition. However, there is difficulty to catch keypoints on the face due to complex influences from original images, and there is no guidance to suitable algorithms. In this paper, we study different algorithms that can be applied to locate keyponits. Specifically: our framework (1)prepare the data for further investigation (2)Using PCA and LBP to process the data (3) Apply different algorithms to analysis data, including linear regression models, tree based model, neural network and convolutional neural network, etc. Finally we will give our conclusion and further research topic. A comprehensive set of experiments on dataset demonstrates the effectiveness of our framework.

Using established principles from Information Theory and Statistics, we show that in a deep neural network invariance to nuisance factors is equivalent to information minimality of the learned representation, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. We then show that, in order to avoid memorization, we need to limit the quantity of information stored in the weights, which leads to a novel usage of the Information Bottleneck Lagrangian on the weights as a learning criterion. This also has an alternative interpretation as minimizing a PAC-Bayesian bound on the test error. Finally, we exploit a duality between weights and activations induced by the architecture, to show that the information in the weights bounds the minimality and Total Correlation of the layers, therefore showing that regularizing the weights explicitly or implicitly, using SGD, not only helps avoid overfitting, but also fosters invariance and disentangling of the learned representation. The theory also enables predicting sharp phase transitions between underfitting and overfitting random labels at precise information values, and sheds light on the relation between the geometry of the loss function, in particular so-called "flat minima," and generalization.

Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. First off, let's look at some of the results/failures from my experiments (scroll to the bottom for the final result). Today, colorization is done by hand in Photoshop. To appreciate all the hard work behind this process, take a peek at this gorgeous colorization memory lane video. In short, a picture can take up to one month to colorize. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners. Yet, if you're new to deep learning terminology, you can read my previous two posts [1][2] and watch Andrej Karpathy's lecture for more background.

Deep learning systems have long been tough to work with, due to all the fine-tuning and knob-twiddling needed to get good results from them. Gluon is a joint effort by Microsoft and Amazon Web Services do reduce all that fiddling effort. Gluon works with the Apache MXNet and Microsoft's Cognitive Toolkit frameworks to optimize deep-learning network training on those systems. The problem with steps 1 and 2 is that they're tedious and inflexible. Hard-coding a network is slow, and altering that coding to improve the network's behavior is also slow.

Attention was presented by Dzmitry Bahdanau, et al. in their paper "Neural Machine Translation by Jointly Learning to Align and Translate" that reads as a natural extension of their previous work on the Encoder-Decoder model. Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode each output time step. This issue is believed to be more of a problem when decoding long sequences. A potential issue with this encoder–decoder approach is that a neural network needs to be able to compress all the necessary information of a source sentence into a fixed-length vector. This may make it difficult for the neural network to cope with long sentences, especially those that are longer than the sentences in the training corpus.

For data scientists looking to apply Apache Spark's advanced analytics techniques and deep learning models at scale, Databricks is happy to provide The Data Scientist's Guide to Apache Spark. This eBook features excerpts from the larger Definitive Guide to Apache Spark that will be published later this year.