Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, such as principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are all shown to be shallow learners. Their deep learning counterparts exploit multiple deep layers of data reduction which provide predictive performance gains. Stochastic gradient descent (SGD) training optimisation and Dropout (DO) regularization provide estimation and variable selection. Bayesian regularization is central to finding weights and connections in networks to optimize the predictive bias-variance trade-off. To illustrate our methodology, we provide an analysis of international bookings on Airbnb. Finally, we conclude with directions for future research.

Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

The board game Go is older and more complex than chess. While it's been 20 years since IBM's Deep Blue beat world chess champion Garry Kasparov, computers only started beating Go experts a few years ago. An Oct. 18 report in the science journal Nature tells us that this particular man/machine contest is done. A system built by the DeepMind...

The biggest acquisition in the history of technology has been tabled. Broadcom, which itself was purchased by Singapore's Avago Technologies in 2016, has made a $130 billion bid for rival chipmaker Qualcomm. If it goes through (and that's a big if), Broadcom would be paying 20 times the amount Candy Crush-maker King was purchased for, or more than 130 times the amount it cost Facebook to buy Instagram. It could even get the equivalent of five LinkedIns for the price. The proposed deal is so big it's nearly double the biggest tech buyout of all time, Dell's $67bn buyout of EMC in 2015. Broadcom's purchase of Qualcomm would make the company dominant in the chipmaking industry.

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference? There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). There certainly is a massive uptick of articles about AI being a competitive game changer and that enterprises should begin to seriously explore the opportunities. The distinction between AI, ML and DL are very clear to practitioners in these fields. AI is the all encompassing umbrella that covers everything from Good Old Fashion AI (GOFAI) all the way to connectionist architectures like Deep Learning.

From healthcare and security to marketing personalization, despite being at the early stages of development, machine learning has been changing the way we use technology to solve business challenges and everyday tasks. This potential has prompted companies to start looking at machine learning as a relevant opportunity rather than a distant, unattainable virtue. We've already discussed machine learning as a service tools for your ML projects. But now let's look at free and open source software that allows everyone to board the machine learning train without spending time and resources on infrastructure support. The term open source software refers to a tool with a source code available via the Internet for free.

If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today. Geoffrey Hinton is known as the father of "deep learning." Back in the 50s the idea of deep neural networks began to surface and, in theory, could solve a vast amount of problems. However, nobody was able to figure out how to train them and people started to give up.

In an editorial posted on Intel's news site, Intel CEO Brian Krzanich announced they would be releasing the company's first AI processor before the end of 2017. The new chip, formally codenamed "Lake Crest," will be officially known as the Nervana Neural Network Processor, or NNP, for short. As implied by its name, the chip will use technology from Nervana, an AI startup Intel acquired for more than $350 million last year. Unlike GPUs or FPGAs, NNP is a custom-built coprocessor aimed specifically at deep learning, that is, processing the neural networks upon which these applications are based. In that sense, Intel's NNP is much like Google's Tensor Processing Unit (TPU), a custom-built chip the search giant developed to handle much its own deep learning work.

Andrej Karpathy has an article "Software 2.0" that makes the argument that Neural Networks (or Deep Learning) is a new kind of software. I do agree that there indeed a trend towards "teachable machines" as opposed to the more conventional programmable machines, however I do have an issue with some of the benefits that Karpathy mentions to back-up his thesis. Certainly Deep Learning is already eating the Machine Learning world with advances across the board. Karpathy mentions several well known ones: visual recognition, speech recognition, speech synthesis, machine translation, robotics and games. This frames his argument about the sea change in computing and perhaps its time to think about a new kind of software (I guess the kind that you teach like a dog instead of programming).

Rhegmatogenous retinal detachment (RRD) is a highly curable condition if properly treated early1, 2; however, if it is left untreated and develops proliferative changes, it becomes an uncontrollable condition called proliferative vitreoretinopathy (PVR). PVR is a serious condition that can result in blindness regardless of repeated treatments3,4,5. It is important, therefore, for patients to be seen and treated at a vitreoretinal centre at the early RRD stage to preserve visual function. However, establishing such vitreoretinal centres that provide advanced ophthalmological procedures is not practical because of rising social security costs, a problem that is troubling many nations around the world6. On the other hand, medical equipment has made remarkable advances, and one such advancement is the ultra–wide-field scanning laser ophthalmoscope (Optos 200Tx; Optos PLC, Dunfermline, United Kingdom).