The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. Cool stuff, but--especially for someone hoping to explore machine learning for the first time--TensorFlow can be a lot to take in. Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!

The companies have integrated the automation technology into Watchwith's data-driven advanced advertising products. "What used to potentially require thousands of man-hours is now an automated process within the Watchwith platform," Watchwith says in a statement. By embedding artificial intelligence into the video advertising inventory creation process, Watchwith MAF gives TV networks and premium video publishers the power to create, manage and sell contextually relevant native video advertising at scale. "And the result is the highly scalable, native digital video advertising solution the TV industry needs to compete with Facebook, YouTube, Snapchat and other native digital video distribution platforms."

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

As a result, Arcas and his team are now able to teach these machines how to express their perceptions in ways of which we would've never thought possible. Many today might even deny that computers are able to be creative, claiming that only humans are uniquely capable of such. Though, as Arcas noted in his talk, "perception and creativity are by no means uniquely human. We start to have computer models that can do exactly these sorts of things. And that ought to be unsurprising; the brain is computational."

Asking a bunch of literate people about the Great American Novel is an open invitation for us all to show off and make cogent, compelling arguments about the importance of [insert a favorite novel here] in the canon of American literature, regardless of whether anyone outside our small circle of literary compatriots knows of the novel or would agree. As a science fiction and fantasy writer, for example, I can make a pretty good argument for Philip K. Dick's "The Man in the High Castle" or Ray Bradbury's "Fahrenheit 451," or maybe even Mark Helprin's "Winter's Tale," and I might even get a cheering section behind the choice. Ubiquity: It has to be a novel that a relatively large number of Americans have read, and that a large proportion of those who haven't read it know about in other ways (for example, by a popular filmed adaptation). Notability: There has to be a general agreement that the novel is significant -- it has literary quality and/or is part of the cultural landscape in a way that's unquestionable (even if critically assailable). Morality: It needs to address some unique aspect of the American experience, usually either our faults or our aspirations as a nation, with recognizable moral force (not to be confused with a happy ending).

Artificial Intelligence is developing at a rapid pace, with companies examining its potential to propel business growth. According to CNBC, Google has stated that it aims to win the artificial intelligence game, following news that it had purchased UK artificial intelligence firm Deepmind for more than US 537 million at the beginning of 2014. Artificial intelligence is still developing in the container terminal industry, however, intelligent autonomous machines are used at some fully automated terminals globally. These machines will be controlled by a central terminal operating system (TOS), which will coordinate the movement of automated cranes and other such equipment. Simulation and emulation company TBA are currently developing simulated virtual training techniques to allow terminal operator staff to train in a virtual environment.

For warehouse workers and truck drivers, the future does not look rosy. But those of us with more white collar gigs don't have to panic yet, right? Well, maybe some of us do. Sorry, lawyers, we know you're already spinning from a lousy job market and tons of law school debt, but it looks like you're going to face competition from artificial intelligence sooner rather than later. The brainchild of London-born Stanford University student Joshua Browder, the artificial intelligence-driven chatbot has already gotten 160,000 people out of parking tickets.

Machine learning is ubiquitous and used to make critical business and life decisions every day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments. This session explores the fundamentals of machine learning using MATLAB . Rory reviews typical workflows for both supervised (classification and regression) and unsupervised learning, through examples. This presentation demonstrates examples of new functionality in Statistics and Machine Learning Toolbox and Neural Network Toolbox .

In a TEDx talk entitled "How Computers are Learning to Be Creative", Blaise Agüera y Arcas, Google principal scientist, demonstrated how neural networks recognizing images can run them in reverse-- thus generating them. Of this, he noted that perception and creativity are highly linked together. With Google's neural network models, machine perception and machine creativity are no longer that far-fetched. Arcas identified perception as the process by which simple objects are transformed by the mind into overwhelmingly different concepts. With today's technology, even computers are capable of perception. Creativity, on the other hand, is actually-- as far as Arcas is concerned in his speech-- the "flip side" of the former.

MLPB is meant to become an organized collection of machine learning problems and solutions. I need to classify something as A, B or C using a combination of numeric and categorical features. If I could find a similar problem, maybe I could modify the solution to work for my needs. This is where MLPB steps in. Want to see ML problems with sparse data?