This tutorial is intended for someone who wants to understand how Recurrent Neural Network works, no prior knowledge about RNN is required. We will implement the most simple RNN model – Elman Recurrent Neural Network. To get a better understanding of RNNs, we will build it from scratch using Pytorch tensor package and autograd library. I assume that you have some understanding of feed-forward neural network if you are new to Pytorch and autograd library checkout my tutorial. An Elman network was introduced by Jeff Elman, and was first published in a paper entitled Finding structure in time.

Data science is transforming many industries, from health care to banking to heavy manufacturing, and women are leading the charge. That was the crux of the Cambridge Women in Data Science Conference, held March 5 as part of a global event launched by Stanford University in 2015 to educate, inspire, and connect women in tech. The local conference, now in its second year, was hosted by the Institute of Applied Computational Science (IACS) at the Harvard John A. Paulson School of Engineering and Applied Sciences; the MIT Institute for Data, Systems, and Society; and Microsoft. Distinguished speakers from academia and industry presented technical talks to more than 240 female technologists, researchers, and students, highlighting research in such areas as deep learning applications in oncology, data science tools for pollution monitoring, and the challenges of preventing bias in algorithms. In addition, local winners of an international datathon/kaggle challenge, held in conjunction with Stanford's global conference were announced, and students presented posters and took advantage of networking and recruiting opportunities.

In response to the increasing use of artificial intelligence (AI) technologies to defend against cyber attacks, malicious actors are now discussing their potential application for criminal use. Research from Control Risks, the global risk consultancy, has shown that the development of techniques to use these technologies and tools to enhance their capabilities is now increasingly on the agenda of cyber threat actors. Nicolas Reys, associate director and head of Control Risks' cyber threat intelligence team, explained: "More and more organisations are beginning to employ machine learning and artificial intelligence as part of their defences against cyber threats. Cyber threat actors are recognising the need to advance their skills to keep up with this development. One application could be to use deep learning algorithms to improve the effectiveness of their attacks. This shows that AI and its subsets will play a larger role in facilitating cyber attacks in the near future."

We are looking for a Postdoctoral Research Associate with a background in electrical engineering, physics, statistics or computer science to work on a research project involving the application of machine learning techniques to neuroanatomical data. The project will lead to the development of a practical and flexible web-based tool for measuring neuroanatomical alterations in any brain-based disorders. The successful applicant will have previous experience in the application of machine learning - including both shallow and deep learning algorithms - to neuroimaging data. The post holder will join a multi-disciplinary team including clinicians, neuroscientists, psychologists and computer scientists. The selection process will include a panel interview.

Today Intel unveiled "AI: In Production," a new program that makes it easier for developers to bring their artificial intelligence prototypes to market. Since its introduction last July, the Intel Movidius Neural Compute Stick has gained a developer base in the tens of thousands. Intel AI: In Production means we can expect many more innovative AI-centric products coming to market from the diverse and growing segment of technologies utilizing Intel technology for low-power inference at the edge," said Remi El-Ouazzane, Intel vice president and general manager of Intel Movidius. Once developers have a prototype, the next step is to take it into production, which can be challenging and costly for small companies and entrepreneurs. To make it easier, Intel selected AAEON Technologies, a leading manufacturer of advanced industrial and embedded computing platforms, as the first Intel AI: In Production partner. Through the program, AAEON provides two streamlined production paths for developers integrating the low-power Intel Movidius Myriad 2 Vision Processing Unit (VPU) into their product designs. The first option is the new AI Core from AAEON's UP Bridge the Gap. It is a mini-PCIe module that features an Intel Movidius Myriad 2 VPU designed to work with a wide range of x86 host platforms. The AI Core delivers the low-power, high-performance capabilities of the Intel Movidius Myriad 2 VPU deep neural networks accelerator. It is also compatible with the Intel Movidius Neural Compute SDK software suite already used by thousands of machine learning developers and companies worldwide. For companies requiring further customization, AAEON offers development and board manufacturing services that will allow companies to move from Neural Compute Stick-based prototypes to custom boards in a streamlined manner. Intel Movidius Myriad 2 technology makes AI Core one of the most powerful and versatile AI hardware accelerators for edge computing," said Fabrizio Del Maffeo, AAEON vice president, managing director of AAEON Technology Europe and founder of UP Bridge the Gap.

When Nate Soares psychoanalyzes himself, he sounds less Freudian than Spockian. As a boy, he'd see people acting in ways he never would "unless I was acting maliciously," the former Google software engineer, who now heads the non-profit Machine Intelligence Research Institute, reflected in a blog post last year. "I would automatically, on a gut level, assume that the other person must be malicious." It's a habit anyone who's read or heard David Foster Wallace's "This is Water" speech will recognize. Later Soares realized this folly when his "models of other people" became "sufficiently diverse"--which isn't to say they're foolproof, he wrote in the same post.

The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus is usually very narrow and limited to (i) inference -- i.e. how to efficiently execute already trained models and (ii) image classification networks as the primary benchmark for evaluation. Our primary goal in this work is to break this myopic view by (i) proposing a new benchmark for DNN training, called TBD (TBD is short for Training Benchmark for DNNs), that uses a representative set of DNN models that cover a wide range of machine learning applications: image classification, machine translation, speech recognition, object detection, adversarial networks, reinforcement learning, and (ii) by performing an extensive performance analysis of training these different applications on three major deep learning frameworks (TensorFlow, MXNet, CNTK) across different hardware configurations (single-GPU, multi-GPU, and multi-machine). TBD currently covers six major application domains and eight different state-of-the-art models. We present a new toolchain for performance analysis for these models that combines the targeted usage of existing performance analysis tools, careful selection of new and existing metrics and methodologies to analyze the results, and utilization of domain specific characteristics of DNN training. We also build a new set of tools for memory profiling in all three major frameworks; much needed tools that can finally shed some light on precisely how much memory is consumed by different data structures (weights, activations, gradients, workspace) in DNN training. By using our tools and methodologies, we make several important observations and recommendations on where the future research and optimization of DNN training should be focused.

A number of results have recently demonstrated the benefits of incorporating various constraints when training deep architectures in vision and machine learning. The advantages range from guarantees for statistical generalization to better accuracy to compression. But support for general constraints within widely used libraries remains scarce and their broader deployment within many applications that can benefit from them remains under-explored. Part of the reason is that Stochastic gradient descent (SGD), the workhorse for training deep neural networks, does not natively deal with constraints with global scope very well. In this paper, we revisit a classical first order scheme from numerical optimization, Conditional Gradients (CG), that has, thus far had limited applicability in training deep models. We show via rigorous analysis how various constraints can be naturally handled by modifications of this algorithm. We provide convergence guarantees and show a suite of immediate benefits that are possible -- from training ResNets with fewer layers but better accuracy simply by substituting in our version of CG to faster training of GANs with 50% fewer epochs in image inpainting applications to provably better generalization guarantees using efficiently implementable forms of recently proposed regularizers.

Optimizing distributed learning systems is an art of balancing between computation and communication. There have been two lines of research that try to deal with slower networks: {\em quantization} for low bandwidth networks, and {\em decentralization} for high latency networks. In this paper, we explore a natural question: {\em can the combination of both decentralization and quantization lead to a system that is robust to both bandwidth and latency?} Although the system implication of such combination is trivial, the underlying theoretical principle and algorithm design is challenging: simply quantizing data sent in a decentralized training algorithm would accumulate the error. In this paper, we develop a framework of quantized, decentralized training and propose two different strategies, which we call {\em extrapolation compression} and {\em difference compression}. We analyze both algorithms and prove both converge at the rate of $O(1/\sqrt{nT})$ where $n$ is the number of workers and $T$ is the number of iterations, matching the {\rc convergence} rate for full precision, centralized training. We evaluate our algorithms on training deep learning models, and find that our proposed algorithm outperforms the best of merely decentralized and merely quantized algorithm significantly for networks with {\em both} high latency and low bandwidth.

Deep convolutional neural networks have achieved remarkable success in the field of computer vision. While far from new [1], the increasing availability of extremely large, labeled datasets along with modern advances in computation with specialized hardware have resulted in state-of-the-art performance in many problems, including essentially all visual learning tasks. Examples include image classification [2], object detection [3], and semantic segmentation [4]. Despite a rich history of practical and theoretical insights about these problems, modern deep learning techniques typically rely on task-agnostic models and poorly-understood heuristics. However, recent work [5-7] has shown that specialized architectures incorporating classical domain knowledge can increase parameter efficiency, relax training data requirements, and improve performance. Prior to the advent of modern deep learning, optimization-based methods like component analysis and sparse coding dominated the field of representation learning. These techniques use structured matrix factorization to decompose data into linear combinations of shared components. Latent representations are inferred by minimizing reconstruction error subject to constraints that enforce properties like uniqueness and interpretability. Unlike feed-forward alternatives that construct representations in closed-form via independent feature detectors, this optimization-based approach naturally introduces conditional dependence between features in order to best explain data, a useful phenomenon commonly referred to as "explaining away" within the context of graphical models [8].