The excellence of deep learning has also resulted in explorations of several emerging real-world applications, such as self-driving systems [3], automatic machine translations [4], drug discovery and toxicology [5]. The deep learning is based on the structure of deep neural networks (DNNs), which consist of multiple layers of various types and hundreds to thousands of neurons in each layer. Recent evidence has revealed that the network depth is of crucial importance to the success of deep learning, and many deep learning models for the challenging ImageNet dataset are sixteen to thirty layers deep [1]. Deep learning achieves significant improvement in overall accuracy by extracting complex and high-level features at the cost of considerable up-scaling in the model size. In the big data era and driven by the development of semiconductor technology, embedded systems are now becoming an essential computing platform with ever-increasing functionalities. At the same time, researchers around the world from both academia and industry have devoted significant efforts and resources to investigate, improve, and promote the applications of deep learning in embedded systems [6].

Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

Man I love these summary of the year posts. That's really where I learn the most in the shortest time. For example, I stumbled upon this one related to semantic segmentation recently and it's been a huge help. Thank you for posting this.

If you've been following the latest developments in deep learning, you've probably come across artistic style transfer. It's a technique to create a new image with the content of image A, in the style of image B. For example, below is the result of style transfer from a Kandinsky painting to a photo of Neil deGrasse Tyson. Deep learning has also had success in transferring verbal style. Given a 1-minute audio clip of someone talking, Lyrebird is able to capture that person's speaking style and make him say anything by mimicking his voice. I was curious to see if style transfer could also apply to the written word. The idea was to dress up English sentences in the styles of various authors, be it florid poetry or gruff prose, while preserving meaning.

Machines excel at performing a large number of varying tasks. Computers, for example, excel at performing mathematical computations. Even today's slowest personal computer has more computational power than the average human being by a wide margin. As an example, the Apple iPhone 4, released in 2010, had the computational power to perform roughly 1.6 Billion Floating Point Operations per second. As you can imagine, computational power has only increased since then.

Two open source communities--PaddlePaddle, the deep learning framework originated in Baidu, and Kubernetes, the most famous containerized application scheduler--are announcing the Elastic Deep Learning (EDL) feature in PaddlePaddle's new release codenamed Fluid. Fluid EDL includes a Kubernetes controller, PaddlePaddle auto-scaler, which changes the number of processes of distributed jobs according to the idle hardware resource in the cluster, and a new fault-tolerable architecture as described in the PaddlePaddle design doc. Industrial deep learning requires significant computation power. Research labs and companies often build GPU clusters managed by SLURM, MPI, or SGE. These clusters either run a submitted job if it requires less than the idle resource, or pend the job for an unpredictably long time.

Stanford researchers have developed a deep-learning algorithm that evaluates chest X-rays for signs of disease. Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv. "Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there's a lot of variability in the diagnoses radiologists arrive at," said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. "We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses."

Deep learning has given us tremendous new powers to spot patterns hidden in great globs of data. For some challenges, neural networks can even outperform top human experts. However, despite all the progress the new approach represents and the hope that it will lead us to actual artificial intelligence, there are big limits on the practical application of deep learning. Deep learning has emerged as the latest "easy button" for big data analytics. The thinking seems to go like this: Got a lot of data to analyze?

Note: Please access the full code in this GitHub repo. The multitude of methods jointly referred to as "deep learning" have disrupted the fields of machine learning and data science, rendering decades of engineering know-how almost completely irrelevant--or so common opinion would have it. Of all these, one method that stands out in its overwhelming simplicity, robustness, and usefulness is the transfer of learned representations. Especially for computer vision, this approach has brought about unparalleled ability, accessible to practitioners of all levels, and making previously insurmountable tasks as easy as from keras.applications import *. Put simply, the method dictates that a large data set should be used in order to learn to represent the object of interest (image, time-series, customer, even a network) as a feature vector, in a way that lends itself to downstream data science tasks such as classification or clustering.

Google on Monday released DeepVariant, an artificial intelligence tool that uses gene sequencing data to build a more accurate genomic model. Google on Monday released DeepVariant, an artificial intelligence (AI) tool that uses gene sequencing data to build a more accurate genomic model by automatically spotting small insertion and deletion mutations and single-base-pair mutations. The Google Brain team compiled millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle project, feeding the data to a deep-learning system and modifying the parameters of the model until it learned to interpret sequenced data with very high accuracy. "DeepVariant...demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems," says Deep Genomics CEO Brendan Frey. Frey predicts AI will ultimately transcend its ability to help sequence genomic data.