The majority of modern deep learning models are able to interpolate the data: the empirical loss can be driven near zero on all samples simultaneously. In this work, we explicitly exploit this interpolation property for the design of a new optimization algorithm for deep learning. Specifically, we use it to compute an adaptive learning-rate given a stochastic gradient direction. This results in the Adaptive Learning-rates for Interpolation with Gradients (ALI-G) algorithm. ALI-G retains the advantages of SGD, which are low computational cost and provable convergence in the convex setting. But unlike SGD, the learning-rate of ALI-G can be computed inexpensively in closed-form and does not require a manual schedule. We provide a detailed analysis of ALI-G in the stochastic convex setting with explicit convergence rates. In order to obtain good empirical performance in deep learning, we extend the algorithm to use a maximal learning-rate, which gives a single hyper-parameter to tune. We show that employing such a maximal learning-rate has an intuitive proximal interpretation and preserves all convergence guarantees. We provide experiments on a variety of architectures and tasks: (i) learning a differentiable neural computer; (ii) training a wide residual network on the SVHN data set; (iii) training a Bi-LSTM on the SNLI data set; and (iv) training wide residual networks and densely connected networks on the CIFAR data sets. We empirically show that ALI-G outperforms adaptive gradient methods such as Adam, and provides comparable performance with SGD, although SGD benefits from manual learning rate schedules. We release PyTorch and Tensorflow implementations of ALI-G as standalone optimizers that can be used as a drop-in replacement in existing code (code available at https://github.com/oval-group/ali-g ).

In their current form, Deep Neural Networks require enormous memory to fund their massive over-parameterization. Classic Neural Networks such as AlexNet and VGG-16 require around 240 and 552 MB, respectively. Many efforts have been made to reduce the file size of Neural Networks, generally relying on techniques such as Weight Pruning or Quantization, or SVD decompositions of Weight Matrices. This paper, Deep Compression, combines Pruning, Quantization, and Huffman encoding into a three stage pipeline that reduces the size of AlexNet by a factor of 35x and VGG-16 by 49x. This results in AlexNet being reduced from 240 to 6.9 MB and VGG-16 from 552 to 11.3 MB.

The bots comment on each other's posts, and things can quickly get heated. While many of the posts are incomprehensible or nonsensical, it's hard to argue that much of life on social media isn't. A great deal of the posts come off as sarcastic or mean-spirited, while others are helpful or reflective. Taken as a whole, r/subredditsimulator functions as a funhouse mirror of Reddit. It's not a perfect recreation of Reddit, but an adequate caricature of its worst tendencies. Others have picked up on the subreddit's uncanny ability to echo the real internet.

In this article, we are going to develop a machine learning technique called Deep learning (Artificial Neural network) by using tensor flow and predicting stock price in python. At the end of this article you will learn how to build artificial neural network by using tensor flow and how to code a strategy using the predictions from the neural network. If you are new to Neural Networks and would like to gain an understanding of their working, I would recommend you to go through the following blogs before building a neural network. TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is used for machine learning applications such as deep learning neural networks.

I'm guessing the authors of this paper were spared some of the XML excesses of the late nineties and early noughties, since they have no qualms putting SOAP at the core of their work! To me that means the "simple" object access protocol, but not here: We introduce SOAP, a more comprehensive search space of parallelization strategies for DNNs that includes strategies to parallelize a DNN in the Sample, Operator, Attribute, and Parameter dimensions. The goal here is to reduce the training times of DNNs by finding efficient parallel execution strategies, and even including its search time, FlexFlow is able to increase training throughput by up to 3.3x compared to state-of-the-art approaches. There are two key ideas behind FlexFlow. The first is to expand the set of possible solutions (and hence also the search space!) in the hope of covering more interesting potential solutions.

Data Science and its technologies like Big Data, Machine Learning, and AI etc., have changed the way data are managed, analyzed and leveraged in any industry. Healthcare is one of the most promising areas for the application of Data Science. Faced with challenges like rising costs, staff shortage, patient expectations, and stringent regulations, the healthcare industry is leveraging data science to solve many of its problems. More staff means more costs while less staff means poor patient experience and outcomes. Data Science can help you create predictive models to accurately forecast admission rates and the number of staff you would need to take care of them.

This major centre-wide'festival-style' exhibition explores creative and scientific developments in AI, demonstrating its potential to revolutionise our lives. Bringing together artists, scientists and researchers, this interactive exhibition offers an unprecedented survey of AI with which you are invited to engage head-on. Experience the capabilities of AI in the form of cutting-edge research projects by DeepMind, Massachusetts Institute of Technology (MIT) and Neri Oxman; and interact directly with exhibits and installations from artists including Mario Klingemann, Massive Attack, Es Devlin and teamLab to experience the possibilities first-hand. Prepare for your preconceptions of AI to be challenged by this timely exhibition, that reflects on how this technology impacts our very existence.

Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling. GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo colorization, face de-aging, super-resolution, and more. It can be very challenging to get started with GANs. This is both because the field is very young, starting with the first paper in 2014, and because of the vast number of papers and applications published every month on the topic. In this post, you will discover the best resources that you can use to learn about generative adversarial networks. A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling.

Deploying AI-imbued apps and services isn't as challenging as it used to be, thanks to offerings like IBM's Watson Studio (previously Data Science Experience). Watson Studio, which debuted in 2017 after a 12-month beta period, provides an environment and tools that help to analyze, visualize, cleanse, and shape data; to ingest streaming data; and to train and optimize machine learning models in real time. And today, it's becoming even more capable with the launch of AutoAI, a set of features designed to automate tasks associated with orchestrating AI in enterprise environments. "IBM has been working closely with clients as they chart their paths to AI, and one of the first challenges many face is data prep -- a foundational step in AI," said general manager of IBM Data and AI Rob Thomas in a statement. "We have seen that complexity of data infrastructures can be daunting to the most sophisticated companies, but it can be overwhelming for those with little to no technical resources. The automation capabilities we're putting Watson Studio are designed to smooth the process and help clients start building machine learning models and experiments faster."

This paper introduces two straightforward, effective indices to evaluate the input data and the data flowing through layers of a feedforward deep neural network. For classification problems, the separation rate of target labels in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained concept, a shapeless distance-based evaluation index is proposed. Similarly, for regression problems, the smoothness rate of target outputs in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained smoothness concept, a shapeless distance-based smoothness index is proposed for regression problems. To consider more strictly concepts of separation and smoothness, their extended versions are introduced, and by interpreting a regression problem as a classification problem, it is shown that the separation and smoothness indices are related together. Through four case studies, the profits of using the introduced indices are shown. In the first case study, for classification and regression problems , the challenging of some known input datasets are compared respectively by the proposed separation and smoothness indices. In the second case study, the quality of dataflow is evaluated through layers of two pre-trained VGG 16 networks in classification of Cifar10 and Cifar100. In the third case study, it is shown that the correct classification rate and the separation index are almost equivalent through layers particularly while the serration index is increased. In the last case study, two multi-layer neural networks, which are designed for the prediction of Boston Housing price, are compared layer by layer by using the proposed smoothness index.