A neural network is built around simple linear equations like Y WX B, which contain something called as weights W. These weights get multiplied with the input X and thus plays a crucial in how the model predicts. Most of the computations in deep neural networks are multiplications between float-valued weights and float-valued activations during the forward inference. The prediction scores can even go downhill if a wrong weight gets updated and as the network gets deeper i.e addition of more layers and columns of connected nodes, the error gets magnified and the results miss the target. To make models lighter while not keeping the efficiency intact, many solutions have been developed, and one such solution is neural compression.

A type of computational analysis that works to analyze typing patterns may help in detecting motor signs of Parkinson's disease at early stages, the researchers who developed the analysis report. This new method, which appeared to allow researchers to discriminate between people with early Parkinson's and those without the disease, may also speed data collection and analysis of disease states across neurodegenerative ills. The study, "Classification of Short Time Series in Early Parkinson's Disease With Deep Learning of Fuzzy Recurrence Plots," was published in the IEEE/CAA Journal of Automatica Sinica. Objective measures of Parkinson's motor signs are crucial for diagnosing the disease early and correctly, as well as for monitoring progression and assessing treatment response. Early detection of Parkinson's disease (PD) is particularly relevant, as people at early stages of the disease are more likely to benefit from neuroprotective treatments.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks.

Machine learning and deep learning are both forms of artificial intelligence. You can also say, correctly, that deep learning is a specific kind of machine learning. Both machine learning and deep learning start with training and test data and a model and go through an optimization process to find the weights that make the model best fit the data. Both can handle numeric (regression) and non-numeric (classification) problems, although there are several application areas, such as object recognition and language translation, where deep learning models tend to produce better fits than machine learning models. Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm).

Learn how Facebook and Intel are utilizing 2nd generation Intel Xeon Scalable processors with Intel Deep Learning Boost technology to optimize deep learning frameworks such as PyTorch! About Intel Software: The Intel Developer Zone encourages and supports software developers that are developing applications for Intel hardware and software products. The Intel Software YouTube channel is a place to learn tips and tricks, get the latest news, watch product demos from both Intel, and our many partners across multiple fields. You\'ll find videos covering the topics listed below, and to learn more, you can follow the links provided! Follow INTEL SOFTWARE on TWITTER: http://bit.ly/2zahGSn

In order to tackle the problem of abstractive summarization of long multi-sentence texts, it is critical to construct an efficient model, which can learn and represent multiple compositionalities better. In this paper, we introduce a temporal hierarchical pointer generator network that can represent multiple compositionalities in order to handle longer sequences of texts with a deep structure. We demonstrate how a multilayer gated recurrent neural network organizes itself with the help of an adaptive timescale in order to represent the compositions. The temporal hierarchical network is implemented with a multiple timescale architecture where the timescale of each layer is also learned during the training process through error backpropagation through time. We evaluate our proposed model using an Introduction-Abstract summarization dataset from scientific articles and the CNN/Daily Mail summarization benchmark dataset.

The Artificial Neural Networks (ANN) have evolved tremendously with a variety of networks to suit for applications according their individual properties. The ANN have a simple structure consisting of nodes (also called processing units) connected to each other via weights. The network gets stimulated by giving input to few are all nodes, and this stimulation, also called activation spreads through entire network. The way in which layers are connected and fed categorizes ANNs in to feed-forward networks (FFN) or feed-back networks (FBN). The FFNs are acyclic in nature i.e. just one forward travelling of weights and biases; whereas the FBNs are cyclically connected i.e. some layers have a connection coming from the other layers recursively.

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms, but also energy-efficient computational models when implemented in VLSI circuits. In this paper, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultra-high energy efficiency can be achieved. We also propose several techniques to improve the performance on a recognition task, and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST dataset. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been analyzed empirically, but it has never before been mathematically derived. The softmax function partitions the transformed input space into cones, each of which encompasses a class. This is equivalent to putting a number of centroids in this transformed space at equal distance from the origin, and k-means clustering the data points by proximity to these centroids. Softmax only cares in which cone a data point falls, and not how far from the centroid it is within that cone. We formally prove that networks with a small Lipschitz modulus (which corresponds to a low susceptibility to adversarial attacks) map data points closer to the cluster centroids, which results in a mapping to a k-means-friendly space. To leverage this knowledge, we propose Centroid Based Tailoring as an alternative to the softmax function in the last layer of a neural network. The resulting Gauss network has similar predictive accuracy as traditional networks, but is less susceptible to one-pixel attacks; while the main contribution of this paper is theoretical in nature, the Gauss network contributes empirical auxiliary benefits.

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We substantiate our discussion with experiments on well-known benchmark data sets to showcase the difficulty of finding good trade-offs between resource-efficiency and predictive performance.