Special note: we're teaching a fully updated part 1, in person, for seven weeks from Oct 30, 2017, at the USF Data Institute. See the course page for details and application form. Part 1 of the course has now been viewed by tens of thousands of students, introducing them to nearly all of today's best practices in deep learning, and providing many hours of hands-on practical coding exercises. We have collected some stories from graduates of part 1 on our testimonials page. Today, we are launching Part 2: Cutting Edge Deep Learning for Coders.

Back in the days when a Neural Network was that scary, hard-to-learn thing which was rather a mathematical curiosity than a powerful Machine Learning or Artificial Intelligence tool - there were surprisingly many relatively successful applications of classical data mining algorithms in Natural Language Processing (NLP) domain. It seemed that problems like spam filtering or Part of Speech Tagging could be solved using rather easy and understandable models. But not every problem can be solved this way. Simple models fail to properly capture linguistic subtleties like irony (although humans often fail at that one too), idioms or context. Algorithms based on overall summarization (e.g.

We have discussed several times about the impact of Artificial Intelligence (AI) on threat landscape, from a defensive perspective new instruments will allow the early detections of malicious patterns associated with threats, from the offensive point of view machine learning tools can be exploited to create custom malware that defeats current anti-virus software. At the recent DEF CON hacking conference, Hyrum Anderson, technical director of data science at security shop Endgame, demonstrated how to abuse a machine learning system to create malicious code that can avoid detections of security solutions. Anderson adapted the Elon Musk's OpenAI framework to create malware, the principle is quite simple because the system he created just makes a few changes to legitimate-looking code and convert them into malicious code. A few modifications can deceive AV engines, the system created by the experts was named OpenAI Gym. "All machine learning models have blind spots," he said.

Microsoft has been using field-programmable gate arrays (FPGAs) to improve performance and efficiencies of Bing and Azure for the last few years. But next year, Microsoft plans to make this kind of FPGA processing power available to developers who will be able to use it to run their own tasks, including intensive artificial-intelligence ones, like deep-neural-networking (DNN). At its Build developers conference this Spring, Azure CTO Mark Russinovich outlined Microsoft's big-picture plans for delivering "Hardware Microservices" via the Azure cloud. Russinovich told attendees that once Microsoft solves some lingering security and other issues, "we will have what we consider to be a fully configurable cloud." "This is the core of an AI cloud," Russinovich said, and "a major step toward democratizing AI with the power of FPGA." (A good recap of Russinovich's remarks can be found in this TheNewStack article.)

Welcome to a new section in our Machine Learning Tutorial series: Deep Learning with Neural Networks and TensorFlow. The artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network). The Artificial Neural Network, which I will now just refer to as a neural network, is not a new concept. The idea has been around since the 1940's, and has had a few ups and downs, most notably when compared against the Support Vector Machine (SVM). For example, the Neural Network was popularized up until the mid 90s when it was shown that the SVM, using a new-to-the-public (the technique itself was thought up long before it was actually put to use) technique, the "Kernel Trick," was capable of working with non-linearly separable datasets.

Facebook has shut down two artificial intelligences that appeared to be chatting to each other in a strange language only they understood. The two chatbots came to create their own changes to English that made it easier for them to work – but which remained mysterious to the humans that supposedly look after them. The bizarre discussions came as Facebook challenged its chatbots to try and negotiate with each other over a trade, attempting to swap hats, balls and books, each of which were given a certain value. But they quickly broke down as the robots appeared to chant at each other in a language that they each understood but which appears mostly incomprehensible to humans. The robots had been instructed to work out how to negotiate between themselves, and improve their bartering as they went along.

AXA, the large global insurance company, has used machine learning in a POC to optimize pricing by predicting "large-loss" traffic accidents with 78% accuracy. The TensorFlow machine-learning framework has been open source since just 2015, but in that relatively short time, its ecosystem has exploded in size, with more than 8,000 open source projects using its libraries to date. This increasing interest is also reflected by its growing role in all kinds of image-processing applications (with examples including skin cancer detection, diagnosis of diabetic eye disease and even sorting cucumbers), as well as natural-language processing ones such as language translation. We're also starting to see TensorFlow used to improve predictive data analytics for mainstream business use cases, such as price optimization. For example, in this post, I'll describe why AXA, a large, global insurance company, built a POC using TensorFlow as a managed service on Google Cloud Machine Learning Engine for predicting "large-loss" car accidents involving its clients.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

The pace of technology-driven change is accelerating for enterprises all around the world. While the idea of artificial intelligence (AI) has been around for nearly 70 years, it wasn't until 2017 that we found 72 percent of business leaders believed AI to be a competitive advantage in the future (if not already), according to a recent PwC AI survey. In response, it's critical for companies to iteratively shift paradigms from legacy approaches to better compete in the age of digital transformation. Evolving software algorithms, capable of performing tasks typically requiring human intelligence, are fueling a wave of advancements in visual perception, speech recognition, decision-making, language translation, robotics and autonomous vehicle capability. Though AI is the catchphrase for numerous subfields, machine learning and deep learning are garnering the most attention as they teach themselves to learn, reason, plan and ultimately become more intelligent when exposed to bigger, more refined data sets and a standard predictive analytics model.