Design, create and deploy new features for our state-of-the-art Machine Learning-as-a-Service platform. These features span both front- and back-end functionality, such as the containerization and intelligent scheduling of highly interesting deep learning workloads.

Massachusetts General Hospital is buying into deep learning artificial intelligence, and it will use Nvidia's new DGX-1 deep-learning supercomputer. Nvidia is partnering with the MGH Clinical Data Science Center, which wants to advance health care with AI to improve the detection, diagnosis, treatment, and management of diseases. "Deep learning is revolutionizing a wide range of scientific fields," said Jen-Hsun Huang, CEO of Nvidia, at the company's GPUTech event in San Jose, California, today. "There could be no more important application of this new capability than improving patient care.

"Any sufficiently advanced technology is indistinguishable from magic" – Arthur Clarke In the space of technology, this quote has never been so true. Daily discoveries are not uncommon, with yearly revolutions seldom missed. Machine learning has recently been exploding in popularity, with everyone rushing to see how it can benefit their lives, but how did we get to this stage of innovation? This discovery process of machine learning is what you will learn from this article. I was surprised by how much I learned researching machine learning's upbringing, there is enough drama and action to write a novel about it.

First there was Artificial Intelligence (AI), then came machine learning... neural networks and finally cognitive computing technology. But then came multilingual cognitive computing technology. Cogito Studio is a product for developing customised semantic applications for text analytics, including information analysis, categorisation and extraction. Developed by Expert System in the US state of Maryland, Cogito Studio combines a cocktail of AI algorithms for simulating the human ability to read and understand language (semantics) and deep learning techniques (machine learning) to help optimise the creation of applications that are advanced, intelligent and intuitive.

It was always the intention of creating Musio to be a robot with a high level of English to help kids and adults practice their English language skills. Musio's English is second to none which makes Musio an amazing talking companion. One of the features Musio has is the ability to fix any grammatical mistakes someone might have. If you aren't sure if something is correct, you can ask Musio for a suggestion. This is achieved using a combination of advanced Artificial Intelligence algorithms, thanks to MUSE, a deep learning based A.I quantum engine for communication.

In my last article, I discussed the fundamentals of deep learning, where I explained the basic working of a artificial neural network. If you've been following this series, today we'll become familiar with practical process of implementing neural network in Python (using Theano package). I found various other packages also such as Caffe, Torch, TensorFlow etc to do this job. But, Theano is no less than and satisfactorily execute all the tasks. Also, it has multiple benefits which further enhances the coding experience in Python. In this article, I'll provide a comprehensive practical guide to implement Neural Networks using Theano.

Gradient backpropagation (BP) requires symmetric feedforward and feedback connections — the same weights must be used for forward and backward passes. This "weight transport problem'' (Grossberg 1987) is thought to be one of the main reasons to doubt BP's biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter — the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan'' (BM) update rule.

Text simplification (TS) is the technique of reducing the lexical, syntactical complexity of text. Existing automatic TS systems can simplify text only by lexical simplification or by manually defined rules. Neural Machine Translation (NMT) is a recently proposed approach for Machine Translation (MT) that is receiving a lot of research interest. In this paper, we regard original English and simplified English as two languages, and apply a NMT model–Recurrent Neural Network (RNN) encoder-decoder on TS to make the neural network to learn text simplification rules by itself. Then we discuss challenges and strategies about how to apply a NMT model to the task of text simplification.

The past decade has witnessed the rapid development of feature representation learning and distance metric learning, whereas the two steps are often discussed separately. To explore their interaction, this work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification. Given the training images annotated with the labels, we first produce a large number of triplet units, and each one contains three images, i.e. one person and the matched/mismatch references. For each triplet unit, the distance disparity between the matched pair and the mismatched pair tends to be maximized. We solve this objective by building a deep architecture of convolutional neural networks. In particular, the Mahalanobis distance matrix is naturally factorized as one top fully-connected layer that is seamlessly integrated with other bottom layers representing the image feature. The image feature and the distance metric can be thus simultaneously optimized via the one-shot backward propagation. On several public datasets, DARI shows very promising performance on re-identifying individuals cross cameras against various challenges, and outperforms other state-of-the-art approaches.

Exemplar-based models have achieved great success on localizing the parts of semi-rigid objects. However, their efficacy on highly articulated objects such as humans is yet to be explored. Inspired by hierarchical object representation and recent application of Deep Convolutional Neural Networks (DCNNs) on human pose estimation, we propose a novel formulation that incorporates both hierarchical exemplar-based models and DCNNs in the spatial terms. Specifically, we obtain more expressive spatial models by assuming independence between exemplars at different levels in the hierarchy; we also obtain stronger spatial constraints by inferring the spatial relations between parts at the same level. As our method strikes a good balance between expressiveness and strength of spatial models, it is both effective and generalizable, achieving state-of-the-art results on different benchmarks: Leeds Sports Dataset and CUB-200-2011.