Deep learning, a new and growing area of machine learning, is widely regarded as an important step forward on the path toward true artificial intelligence. Tech firms such as Facebook and Google, as well as companies like Bloomberg (which focuses on financial technology and information), are already beginning to incorporate the technology into their product development. And while the exact definition of deep learning is still somewhat fluid, opportunities are growing for experts who can apply the technology to everything from speech recognition to bioinformatics, drug discovery and equities trading. The global analytics firm SAS says deep learning trains computers to perform human-like tasks such as recognizing speech, identifying images or making predictions. "Deep learning can break patterns into sub-components and build models very accurately," Wayne Thompson, the chief data scientist of SAS Data Science Technologies in Cary, N.C., explained.

Theano has more code available online in general (all though the amount of Tensorflow code/tutorials is rapidly increasing), if you learn one you should be able to pick up the other without much hassle.

Note: I posted this question to stackoverflow as well. Support Vector Machines, k-Neighbors Classifiers, Neural Networks, Decision Trees, ...) on the same training set and collects a bunch of performance metrics for each model. Now, most of these are your standard run-of-the-mill metrics like precision, recall, overall accuracy and all that, but some are more complex (or should I say "different"?), for example: I want to find a good way of ranking these models based on user-specified weights for a subset of the aforementioned performance metrics. If a user's goal was to find the model that was least "complex" while still achieving reasonable precision, they would likely assign a higher weight to the "no. of preprocessing steps" attribute and see which model gets ranked highest (probably model 2, but it really depends on the concrete values of the weights of course). So, in short, I am faced with a so-called Multiple-criteria decision-making (MCDM) problem, and I need to solve it.

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Sophisticated chatbots have, in no uncertain terms, emerged as a reality that businesses and consumers must prepare for. The media is all over this one. At the same time, looking at the spaces where these bots operate, the number of separate messaging programs with active users in (or headed into) the billions has created a fragmented marketplace. At the top tier there's WeChat, QQ, WhatsApp, Facebook Messenger, Apple iMessage, Skype, and Viber, with SnapChat, Line, Slack, Kik, Google, and Samsung holding the potential to join the billion user club if and when they see fit to make it a priority. There are hundreds of messaging apps with small and mid-sized user bases, not to mention the vast audiences that prefer SMS, email, and services like Twitter for communication.

The SaaS Co. is a Berlin-made company that scales and executes sales for B2B SaaS products with the help of deep learning. At TSC we find, contact, qualify, and set appointments with decision makers in order to hand them over to you. In this lecture-workshop-breakfast, you will learn how to win as a customer, enterprises like Microsoft, or startups like Twilio. On the other hand we will show you how to use Artificial Intelligence in a practical way in your daily sales processes.

Many people suppose that the development of artificial intelligence may lead to the end of the Human Era. Stephen Hawking, like many other scientists, supports this point of view and believes that works on artificial intelligence can destroy humanity. But many people engaged in medicine oppose his suggestion. Here 5 medicine branches that will only benefit from artificial intelligence application. Scientists are working at using artificial intelligence to predict human body's response to 2 types of breast cancer chemotherapy.

The term "artificial intelligence" is being thrown around a lot lately. But what is artificial intelligence, really? With A.I.'s like Siri, Cortana, and more, the world is approaching what is known as The Singularity, the era of the machine. Though these A.I. are nothing like James Cameron's Skynet in the 1984 sci-fi smash hit The Terminator, it is imperative to understand where artificial intelligence came from in order to fully comprehend where it is going next. Many filmmakers and authors were afraid of the rise of artificial intelligence and attempted to capture this fear in many notable and influential works of fiction that are still relevant today. That being said, not every artificial intelligence system wants world domination, unlike how A.I. has often been portrayed in these works.

Artificial intelligence (AI) and a world in which machines threaten humanity's status quo has been the preserve of science fiction for decades. In the Eighties, Terminator was set in a post-apocalyptic world in which cyborgs rule, RoboCop's protagonist was part-man, part-machine and Short Circuit toyed with the idea of robots developing human-like minds, with rather more endearing results. The reality is the bot has bolted. AI is walking and talking among us. In 2016, we use voice-recognition systems, driverless cars are being trialled and robotic hotel receptionists work in Japan.

Southfield, MI, June 2016 – Stefanini, a 1B global IT provider, announced today that the company is launching Sophie, its artificial intelligence platform with the ability to turn data into valuable solutions. Aware of the latest trends, Stefanini has invested and developed this platform over the last 3 years as a Research & Development and pilot project for clients in Brazil, and now, the company is launching the platform in the United States. "We are very proud to introduce Sophie for our clients in North America, reinforcing Stefanini's commitment to connect people and technology innovations with a goal to create business value," said Antonio Moreira, Stefanini CEO, North America and Asia Pacific. "Our artificial intelligence platform can improve the end-user experience and deliver smarter and more efficient services," affirmed Mr. Moreira. Technology research firm Gartner forecasts that by 2017, autonomics-based managed services and cognitive platforms will fuel a significant reduction in the cost of IT services by automating repetitive tasks currently tackled by humans.