WWDC16 just ended and Apple left us with new amazing innovative APIs. This year speech recognition, proactive applications, machine learning, user intents, and neural networks have been the most frequent terms used during the conference. So, besides a new rich version 3 of Swift, almost every new addition to iOS, tvOS and macOS is related with artificial intelligence. For example, Metal and Accelerate in iOS 10 provide an implementation of convolutional neural networks (CNNs) for the GPU and CPU respectively. During the keynote, Craig Federighi (Apple's SVP Software Engineering) showed how the Photos app on iOS organizes our photos according to different smart criteria. He highlighted that Photos app uses deep learning to provide such functionality. Also, Federighi showed how Siri, now available to developers, can suggest what we need.

Artificial Intelligence start-up Arya.ai announced on Monday the global launch of'Braid, an open Source tool to build intelligence quickly into systems. "Open sourcing key tools in AI, will help discover newer, interesting and more impactful use cases and applications for AI that we may not have even thought of," said Vinay Kumar Sankarapu, CEO and founder of Arya.ai. Technology companies and start-ups trying to create products that use Artificial Intelligence are racing to build neural networks. By their very nature however, neural networks are complex and call for Deep Learning. Building neural networks, which are not unlike actual human brains with their complex layers, is a resource-intensive, expensive and time consuming process.

We won't have robot doctors for a long time, but the human doctors we have now are beginning to lean on specialized artificial intelligence to help save time. Google DeepMind just announced a partnership with University College London Hospital which will explore using artificial intelligence to treat patients with head and neck cancers. The goal is to develop tools to automatically identify cancerous cells for radiology machines. Currently, radiologists employ a manual process, called image segmentation, to take CT and MRI scans and use them to create a map of the patient's anatomy with clear guidelines of where to direct the radiation. Avoiding healthy areas of the head and neck requires that map to be extraordinarily detailed; typically it takes four hours to create.

Autonomous cars need a new kind of horsepower to identify objects, avoid obstacles and change lanes. There's a good chance that will come from graphics processors in data centers or even the trunks of cars. With this scenario in mind, Nvidia has built two new GPUs -- the Tesla P4 and P40 -- based on the Pascal architecture and designed for servers or computers that will help drive autonomous cars. In recent years, Tesla GPUs have been targeted at supercomputing, but they are now being tweaked for deep-learning systems that aid in correlation and classification of data. "Deep learning" typically refers to a class of algorithmic techniques based on highly connected neural networks -- systems of nodes with weighted interconnections among them.

As the title suggests, in this article, we will be taking a look at some of the deep learning models in R. Some of the pioneering advancements in neural networks research in the last decade have opened up a new frontier in machine learning that is generally called by the name deep learning. The general definition of deep learning is, a class of machine learning techniques, where many layers of information processing stages in hierarchical supervised architectures are exploited for unsupervised feature learning and for pattern analysis/classification. The essence of deep learning is to compute hierarchical features or representations of the observational data, where the higher-level features or factors are defined from lower-level ones. Although there are many similar definitions and architectures for deep learning, two common elements in all of them are: multiple layers of nonlinear information processing and supervised or unsupervised learning of feature representations at each layer from the features learned at the previous layer. The initial works on deep learning were based on multilayer neural network models.

Based in New York and Seattle, Euclidean Technologies uses machine learning to evaluate individual companies as potential long-term investments. The company currently has about 100 million under management. Before Michael Seckler and John Alberg created Euclidean, they founded one of the first software-as-a-service (SaaS) companies. In 2006, their company was acquired by ADP for 160 million. They started Euclidean because they faced hard questions about how to manage their money.

There's no doubt that artificial intelligence (AI) is here and is rapidly gaining the attention of brands large and small. As I talk to customers and prospects, they are interested in understanding how AI and its subcomponents (cognitive computing, machine learning, or even deep learning) are being woven into various departments (marketing, sales, service and support) at organizations across industries. Here are some examples of cognitive computing and machine learning today at organizations, and how these capabilities will enhance customer experience in the future. I think it's important to start with a few foundational facts: Cognitive computing enables software to engaging in human-like interactions. Cognitive computing uses analytical processes (voice to text, natural language processing and text and sentiment analysis) to determine answers to questions.

Deep Learning: Keras Short Video Tutorial (youtu.be)

The WaveNet system can be thought of as an improvement upon concatenative text to speech, in that it still employs recordings of real human voices. But instead of chopping these up and reorganizing them in the old way, it uses an artificial neural network to generate synthetic utterances based upon the voices it was trained with. The downside is that this system is computationally intensive. Modeling raw audio typically requires 16,000 samples per second, with each sample being influenced by all the previous ones. This is well beyond the processing power of a typical smartphone, but not unthinkable for GPUs like Nvidia's DGX-1 deep learning supercomputer.

Google's DeepMind artificial intelligence has produced what could be some of the most realistic-sounding machine speech yet. WaveNet, as the system is called, generates voices by sampling real human speech and directly modeling audio waveforms based on it, as well as its previously generated audio. In Google's tests, both English and Mandarin Chinese listeners found WaveNet more realistic than other types of text-to-speech programs, although it was less convincing than actual human speech. If that weren't enough, it can also play the piano rather well. Text-to-speech programs are increasingly important for computing, as people begin to rely on bots and AI personal assistants like Apple's Siri, Microsoft's Cortana, Amazon's Alexa, and the Google Assistant.