Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for R: This link contains a curated list of R tutorials for Data Science, NLP and Machine Learning.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Few things are more interesting than good conversation. And, right now, few things are more worthy of a good conversation than AI. Driven by deep learning -- a technology that's mysterious even to some of the people who have conjured it -- AI is remaking everything in the world around us. Its host is longtime technology journalist Michael Copeland, whose elegant prose once made its home at Wired and Fortune magazines (and now graces our blog). Our goal: To help everyone who knows they need to get smart about AI get their heads around the topic.

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry. The Turing Award, generally regarded as the'Nobel Prize of computing', is an annual prize awarded to "an individual selected for contributions of a technical nature made to the computing community". In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR's Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology. EB: What are the most important examples of Artificial Intelligence in mainstream society today? MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible.

In an evaluation of retinal photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy, according to a study published online by JAMA. Among individuals with diabetes, the prevalence of diabetic retinopathy is approximately 29 percent in the United States. Most guidelines recommend annual screening for those with no retinopathy or mild diabetic retinopathy and repeat examination in 6 months for moderate diabetic retinopathy. Retinal photography with manual interpretation is a widely accepted screening tool for diabetic retinopathy. Automated grading of diabetic retinopathy has potential benefits such as increasing efficiency and coverage of screening programs; reducing barriers to access; and improving patient outcomes by providing early detection and treatment.

Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research. For example, cancer pathology reports, text documents that describe cancerous tissue in detail, must be individually read and annotated by experts before becoming part of a cancer registry.

Dubbed as Amazon AI, the new service offers powerful AI capabilities such as image analysis, text to speech conversion, and natural language processing. On the analytics front, Amazon is adding a new interactive, serverless query service called Amazon Athena that can be used to retrieve data stored in Amazon S3. With this, customers can run and manage workloads in the cloud, seamlessly from existing VMware tools. Extending Lambda to connected devices, AWS has announced AWS Greengrass – an embedded Lambda compute environment that can be installed in IoT devices and hubs.

How The'Silly' Irish Founders At Intercom Built One Of Silicon Valley's Fastest-Growing Businesses At the AWS re:Invent event, Amazon has announced a host of new services that highlight its commitment to enterprises. Andy Jassy, CEO of AWS, emphasized on the innovation in the areas of artificial intelligence, analytics, and hybrid cloud. Amazon has been using deep learning and artificial intelligence in its retail business for enhancing the customer experience. The company claims that it has thousands of engineers working on artificial intelligence to improve search and discovery, fulfillment and logistics, product recommendations, and inventory management. Amazon is now bringing the same expertise to the cloud to expose the APIs that developers can consume to build intelligent applications.

Papers about deep learning ordered by task, date. Current state-of-the-art papers are labelled. RenderGAN: Generating Realistic Labeled Data, nov 2016, arxiv Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, feb 2016, arxiv SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5MB model size, feb 2016, arxiv Snapshot Ensembles: Train 1, Get M for Free, 2016, paper, github

Nvidia cofounder Chris Malachowsky is eating a sausage omelet and sipping burnt coffee in a Denny's off the Berryessa overpass in San Jose. It was in this same dingy diner in April 1993 that three young electrical engineers–Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang–started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. East San Jose was a rough part of town back then–the front of the restaurant was pocked with bullet holes from people shooting at parked cop cars–and no one could have guessed that the three men drinking endless cups of coffee were laying the foundation for a company that would define computing in the early 21st century in the same way that Intel did in the 1990s. "There was no market in 1993, but we saw a wave coming," Malachowsky says. "There's a California surfing competition that happens in a five-month window every year. When they see some type of wave phenomenon or storm in Japan, they tell all the surfers to show up in California, because there's going to be a wave in two days. We were at the beginning."