In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We identify the RPU device and system specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30,000X compared to state-of-the-art microprocessors while providing power efficiency of 84,000 GigaOps/s/W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisted of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration and analysis of multimodal sensory data flows from massive number of IoT (Internet of Things) sensors.

Optimization is a big part of machine learning. Almost every machine learning algorithm has an optimization algorithm at it's core. In this post you will discover a simple optimization algorithm that you can use with any machine learning algorithm. It is easy to understand and easy to implement. Gradient Descent For Machine Learning Photo by Grand Canyon National Park, some rights reserved.

Open source software is an important piece of the data science puzzle. According to the most recent KDnuggets data science software poll results, 73% of data scientists used free software in the previous 12 months. While there are many sources of such tools on the internet, Github has become a de facto clearinghouse for all types of open source software, including tools used in the data science community. The importance, and central position, of machine learning to the field of data science does not need to be pointed out. The following is an overview of the top 10 machine learning projects on Github.* The top project is, unsurprisingly, the go-to machine learning library for Pythonistas the world over, from industry to academia.

Recently we wrote about the top HR blogs that you should follow. All of the blogs we profiled in that post consistently publish great content, but some content is shared far greater than others. Psychologists, SEO and marketing experts have been studying virality and content sharing for years to understand what are the associated triggers that leads to virality. Why are some articles shared while others are ignored? What common traits are there between content from different blogs and news sources that goes viral?

Convolutional Neural Networks can be visualized as computation graphs with input nodes where the computation starts and output nodes where the result can be read. Here the models that are provided with mxnet are compared using the mx.viz.plot_network The output node is at the top and the input node is at the bottom.

Artificial intelligence (AI) is the most promising technology that can change the world, said Facebook's CEO Mark Zuckerberg on Saturday. "Artificial intelligence will understand senses, such as vision and feeling, better than human beings. Its application in daily lives such as autonomous driving will improve the world," Zuckerberg said at the China Development Forum in Beijing. According to him, though it will take a few more years for the cutting-edge technology to be widely used, its potential is huge. They can always maintain their focus.

Gary Marcus March 15, 2016 For nearly half a century, Artificial Intelligence (AI) has been more science fiction than science: exciting, possible, but just out of reach. And despite significant advances, "strong AI" in many ways remains elusive. Best-selling author and entrepreneur Gary Marcus provides a cognitive scientist's perspective on AI. What are we still struggling with? Perhaps most compelling, is there anything programmers of AI can still learn from studying the science of human cognition?

Machine learning technology is now essential to Search, which remains the crown jewel of Google services. Google's most high-profile engineer, Jeff Dean, has said machine learning is the third most important signal, out of hundreds, that determine how Google search results are ranked. Numbers 1 and 2 are a closely-guarded secret. Considering the software being used only developed in 2015, its fast-rising importance has surprised even company insiders. "One of the concerning things is that [machine learning] is so new that even the best engineers don't know the techniques," Schmidt said.

HP Enterprise Bets on'Machine Learning' Cloud Service HP, having backed away from a key portion of the cloud computing-on-demand market, is expanding into cloud services to help companies analyze data such as photos, audio clips and comments on social media.Read more.

Microsoft's brain is now available for anyone to use in their apps. The company has open sourced the artificial intelligence framework it uses to power speech recognition in its Cortana digital assistant and Skype Translate applications. This means that anyone in the world is now free to view, modify, and use Microsoft's code in their own software. The framework, called, CNTK, is based on a branch of artificial intelligence called deep learning, which seeks to help machines do things like recognize photos and videos or understanding human speech by mimicking the structure and functions of the human brain. Tech giants like Microsoft, Google and Facebook have invested heavily in deep learning research for years, going so far as to hire many of academics who pioneered the field.