Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), to implement an effective algorithm for on-line fall detection. The proposed classifier is based on a Recurrent Neural Network (RNN) model with underlying Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained by the SisFall authors.

NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present {\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides layers' mini-batch computation into several micro-batches. Based on Dynamic Programming and Integer Linear Programming, {\mu}-cuDNN enables faster algorithms by decreasing the workspace requirements. At the same time, {\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples statistical efficiency from the hardware efficiency safely. We demonstrate the effectiveness of {\mu}-cuDNN over two frameworks, Caffe and TensorFlow, achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2 GPU. These results indicate that using micro-batches can seamlessly increase the performance of deep learning, while maintaining the same memory footprint.

Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to low-resource language pairs, yet requiring tens of thousands of parallel sentences. In this work, we take this research direction to the extreme and investigate whether it is possible to learn to translate even without any parallel data. We propose a model that takes sentences from monolingual corpora in two different languages and maps them into the same latent space. By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data. We demonstrate our model on two widely used datasets and two language pairs, reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French datasets, without using even a single parallel sentence at training time.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for algorithm learning to achieve greater effectiveness. This practical course contains 35 lectures and 4 hours of content.

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deeplearning4j, Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive introduction to deep learning. We start by presenting some famous success stories and a brief recap of the most common concepts found in machine learning.

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems. In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code.

Use of GPU is fast expanding outside of the 3D video game realm and offering numerous benefits for enterprise as well as industrial applications. With Deep learning taking a center stage in the industrial 4.0 revolution, GPU and x86 CPU manufacturers are ensuring that solution developers are not restrained by the range of options when it comes to choosing the right silicon for their product. So let's review what GPU can do differently from a CPU and vice versa, also how they make the perfect couple in the world of robot surgeons, cryptocurrencies, smart factories and self-driving cars. Let's review one by one and discuss their basic differentiating characteristics. The central processing unit (CPU) of a computer is often referred to as its brain where all the processing and multitasking takes place.

A team of researchers from the University of Washington and the Allen Institute for AI has trained an AI system to respond like a dog using data from an actual animal. In their paper uploaded to the arXiv preprint server, the group describes their system and what it can and cannot do. The team is also going to present their work at the Conference on Computer Vision and Pattern Recognition this summer. AI systems are typically based on deep-learning algorithms that process data describing events, and then using what they have learned to predict future behavior. In this new effort, the researchers have applied such a strategy to dog behavior.

The topic of insurtech is raising growing interest. This is mainly due to the immense size and importance of the insurance market, however, can also be attributed to the promising new opportunities offered by new technologies. The applications of these are very diverse and players in the insurtech space can be roughly divided into five categories. A unifying trait, however, is that many of these insurtechs have the common approach to tackling their problems by leveraging data and artificial intelligence (AI). Here, Mehrdad Piroozrom and Dr Babak Ahmadi discuss this for InsurTech Rising (read the original version here).

Neural Networks are used all around us: they index photos into categories, translate text, suggest replies for emails, and beat the best games. Many people are eager to apply this knowledge to their own data, but many fail to achieve the results they expect. In this course, we'll start by building a simple flower recognition program, making you feel comfortable with Tensorflow, and it will teach you several important concepts in Neural Networks. Next, you'll start working with high-dimensional uses to predict one output: 1275 molecular features you can use to predict the atomization energy of an atom. The next program we'll create is a handwritten number recognition system trained on the famous MNIST dataset.