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www.path.com/jobs We believe in beautiful design and that it elevates us beyond functionality. We believe in fewer meetings, more iteration. We believe that the greatest collaboration can be stumbled upon over a lunchtime walk or late at night, after the rest of the city starts to quiet. We are mothers and fathers and we are fresh out of school. We are runners and cyclists, illustrators and writers. We are lovers of dogs, public art, live music, Mexican food and copious amounts of coffee. We share moments with our close friends and families, everyday. This is why we love working at Path. This is why we come to work everyday, and why we are always looking to add to our diverse and passionate bunch. Have a look at what’s available and apply today at www.path.com/jobs. We can’t wait to meet you!+ More details
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Part 2: Time tracking. See how easy it is to track time remotely, online or offline, and submit timesheets right from the app. Features include one-tap timers, daily/weekly views and a direct link to QuickBooks.+ More details
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Part 1: Project management & staff lists. Introduction to the app’s functions, including one-tap access to clients and staff, managing projects, and the ability to call, text or email clients right from the app.+ More details
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Metafiziq Recordings presents: VARIOUS ARTISTS - "METHAMATHIC" LP (2013) cat. # MTFZ27LP purchase tracks: http://store.metafiziq.org/album/methamathic for more info: https://www.facebook.com/metafiziq https://soundcloud.com/metafiziq http://www.metafiziq.org/+ More details
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The Parodivas, Congrats Jason!, J Alho Photography, Worcester Pride, Peppercorns, New Viewer Asma, Queerspresso, People's Personality in the Mirror, Now You See Me, Mud, Morgan www.parodivas.com www.jalho.us thequeerspresso.wordpress.com rainbowpodsquad.net podcastique.net+ More details
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Listen to our Director of Research, Marsal Gavaldà, explain the mechanisms behind Google's Knowledge Graph, and learn how the idea is not as new as one might think. TRANSCRIPT: I wanted to talk about a technology trend that is revolutionizing how computers understand language. And that is the Knowledge Graph, which is very relevant to the work we do here at Expect Labs. You can visualize the Knowledge Graph as a giant network where the nodes are objects, such as a particular place, an individual person, a specific movie, and these nodes have properties such as the date they were founded, born, or released, and they also have links, from one node to another, that encode relationships such as "similar to", or "is-a", like a dog is a mammal is an animal, or "part-of," such as steering wheel is part of a vehicle. What's fascinating is that the idea of a Knowledge Graph is not totally new. In fact, you can argue that it's an extension of the taxonomies and ontologies that have existed since the dawn of civilization, with, say Aristotle's attempt at categorizing natural phenomena over 2,000 years ago, or the French enciclopédistes like Diderot and d'Alembert who during the Enlightenment in the 18th century attempted to collect and write down an organized compendium of all human knowledge. However, what is new today is the scale of the Knowledge Graph, also the amount of detail, and the fact that is being created in an automated way.+ More details
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Listen to Expect Labs' Research Engineer, Simon Handley, explain a few recent innovations in the field of deep learning. TRANSCRIPT: I'm going to talk today about recent advances in machine learning, particularly deep learning. Deep learning is a way of learning feed forward neural networks. Just to go back a step and get some context: The feed forward neural network is where you're classifying some data, so let's say for example I'm trying to take images or videos and classify them as to what they contain. For example, I may take a particular image and say this contains a cat, a house, a tree, a red car. The way you would do that is that you would take that data and you would label that, and then you would feed that through the network. This neural network has different layers - has an input layer where the whole data comes in, and then that signal feeds up through multiple hidden layers to an output layer and the output layer interpret it as to the classification you want. In the 80s and 90s backpropagation was the main way of doing that, and people found out that that back propagation just didn't work very well and it was very hard to train neural network with more than one with hidden layer. That is to say you'd have 3 layers of the network at the input data, the output data, and a hidden layer in the middle we just learned by the algorithm, and that just turned out that the algorithm just didn't work for more than one hidden layer. And a big innovation came when Geoff Hinton and others discovered that just by multiple layers of unsupervised learning, that give you more intermediate representations was possible to train new networks with maybe like ten different layers, and that was a huge breakthrough, and so with these new techniques, they applied to the various real world problems, like for example - since the latest major use of Android, they've been using deep learning for the speech recognition and the accuracy has vastly improved. Another sort of major advances of open source, one of their tools, and the major advance here with no special tool was different from existing python package that they use, the main innovation was packaged up in a very easy to use way, so any old data scientist could take the tool and put the data through it. Prior to the release of this tool, it was quite difficult to apply deep learning at all.+ More details
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