Computer scientists have spent more than two decades teaching, training and developing machines to see the world around them. Only recently have the artificial eyes begun to match (and occasionally exceed) their biological predecessors. In September of this year, a team of researchers from Google's DeepMind division published a paper outlining the operation of their newest Generative Adversarial Network. Dubbed BigGAN, this image-generation engine leverages Google's massive cloud computing power to create extremely realistic images. But, even better, the system can be leveraged to generate dreamlike, almost nightmarish, visual mashups of objects, symbols and virtually anything else you train the system with.

Over the past few years, the term "deep learning" has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics. And with good reason – it is an approach to AI which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries. Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future. The ever-growing industry which has established itself to sell these tools is always keen to talk about how revolutionary this all is. But what exactly is it?

The report "Deep Learning Market: Global Report (2018 -2023)" provides market intelligence on the different segments based on type, application and geography. Market size and forecast (2018-2023) has been provided in terms of both, Value (USD) and Volume (KG) in the report. A detailed qualitative analysis of the factors responsible for driving and restraining growth of the Deep Learning and future market opportunities have also been discussed. The report covers the present scenario and the growth prospects of the Deep Learning market for 2018-2023. To calculate the market size, the report considers the revenue generated from the sales of the web conferencing and video conferencing, secondary resources and doing in-depth company share analysis of major Top players in the Deep Learning market: IBM Corporation, Qualcomm Technologies, Inc, Microsoft Corporation, Google Inc., General Vision Inc., Hewlett Packard Enterprise, Intel Corporation, Skymind, Baidu Inc., Nvidia Corporation, Sensory Inc.

For decades, games have served as benchmarks for artificial intelligence (AI). In 1996, IBM famously set loose Deep Blue on chess, and it became the first program to defeat a reigning world champion (Garry Kasparov) under regular time controls. But things really kicked into gear in 2013 -- the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. In March 2016, DeepMind's AlphaGo won a three-game match of Go against Lee Sedol, one of the highest-ranked players in the world. And only a year later, an improved version of the system (AlphaZero) handily defeated champions at chess, a Japanese variant of chess called shogi, and Go.

In this tutorial, you will learn how to apply deep learning to perform medical image analysis. Specifically, you will discover how to use the Keras deep learning library to automatically analyze medical images for malaria testing. Such a deep learning medical imaging system can help reduce the 400,000 deaths per year caused by malaria. Today's tutorial was inspired by two sources. They've helped me as I've been studying deep learning. I live in an area of Africa that is prone to disease, especially malaria. I'd like to be able to apply computer vision to help reduce malaria outbreaks. Do you have any tutorials on medical imaging? I would really appreciate it if you wrote one.

The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous analyses of gut microflora revealed associations between specific microbes and host health and disease status, genotype and diet. Here, we developed a method of predicting the biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals (1,663 microbiome samples). Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations.

It is safe to say that there are too many manual processes in medicine. When in training, I write lab scores, diagnoses, and other graphic notes on paper. I always know this is an area where technology can help improve my workflow and hope it will also improve patient care. Since then, progress in electronic medical records has been extraordinary, but the information they provide is not much better than the old paper charts they replaced. If technology wants to improve care in the future, then the electronic information provided to doctors needs to be enhanced by analytical power and machine learning.

A study by Accenture has predicted that growth in the AI healthcare space is expected to touch $6.6 Bn by 2021 with a CAGR of 40%. As on today, Artificial Intelligence and Machine Learning are well and truly poised to make the work of healthcare providers more logical & streamlined than repetitive. The technology is helping shape personalized healthcare services while significantly reducing the time to look for information that is critical to decision making and facilitating better care for patients. Artificial Intelligence in Healthcare has immense potential to improve costs, the quality of services, and access to them. According to CIO, AI-powered healthcare are driving meaningful changes across the entire patient journey.

A report from Accentureclearly highlights those companies that don't want to capitalize on AI will not survive the future (see Figure 1). The AI war is being fought with open source technologies such as TensorFlow, Spark ML, Caffe, Torch and Theano. But wait…if these AI algorithms and technologies – the "Weapons of Mass Business Model Destruction" – are readily available to everyone, what are the sources of business value and differentiation? Increasingly the equation for deriving and driving business value and differentiation isn't having the Machine Learning, Deep Learning and AI frameworks, but is found in two foundational principles: I have already written the book "Big Data MBA: Driving Business Strategies with Data Science" and several blogs on the "Thinking Like A Data Scientist" methodology which provides a thorough, business-driven process for identifying, validating, valuing and prioritizing the organization's business and operational use cases. See the following blogs for more details on the "Thinking Like A Data Scientist" methodology: Now let's consider the other part of the AI equation – monetizing the data.

What's not apparent to many practitioners and researchers in Deep Learning is that the rich variety of methods developed over the past several years are relevant to systems that have different kinds of goals. Deep Learning arose from the Machine Learning community, so it is natural to think of DL networks as systems suitable for performing predictions. Predictions are unfortunately too broad a goal and thus leads to a lack of specificity as to the appropriate methods to fine tune a solution. What I mean here is that you can cast almost any intelligent goals as that of making a prediction. However, to be successful, one has to a minimum understand what kind of prediction is being made and this leads towards a more pragmatic understanding of whether the right tools for the job are used.