I am working on developing a neural network from scratch. Each of my nodes are linear and each layer is fully connected. The neurons fire with Relu activation so the true value of N would be max{0, N}. As of right now all of the framework is complete and I have a simple game that I am testing the neural network on. However I am not getting the results I expect.

Submission statement: Finding the correct learning rate is a pain. But this paper shows how to find reasonable learning rate bounds. Then you can cyclically vary your learning rate to getting better accuracy and often a decreasing training time. P.S There is a PR for this in keras-contrib.

Hi, I'm books editor Carolyn Kellogg, and this week, our books newsletter has gotten a little bit interactive. Maybe you read Han Kang's "The Vegetarian" -- I know I did after it won the Man Booker International Prize. The novel is a spare, strange tale of a South Korean woman who stops eating meat, in an unspoken resistance to her husband, and wastes away. The book was an English-language bestseller and young translator Deborah Smith was lauded with praise. But a closer look at her translation caused a huge controversy in South Korea: Did it go too far?

We are surrounded by hysteria about the future of Artificial Intelligence and Robotics. There is hysteria about how powerful they will become how quickly, and there is hysteria about what they will do to jobs. As I write these words on September 2nd, 2017, I note just two news stories from the last 48 hours. Yesterday, in the New York Times, Oren Etzioni, chief executive of the Allen Institute for Artificial Intelligence, wrote an opinion piece titled How to Regulate Artificial Intelligence where he does a good job of arguing against the hysteria that Artificial Intelligence is an existential threat to humanity. He proposes rather sensible ways of thinking about regulations for Artificial Intelligence deployment, rather than the chicken little "the sky is falling" calls for regulation of research and knowledge that we have seen from people who really, really, should know a little better. Today, there is a story in Market Watch that robots will take half of today's jobs in 10 to 20 years. It even has a graphic to prove the numbers. How many robots are currently operational in those jobs? How many realistic demonstrations have there been of robots working in this arena? Similar stories apply to all the other job categories in this diagram where it is suggested that there will be massive disruptions of 90%, and even as much as 97%, in jobs that currently require physical presence at some particular job site. Mistaken predictions lead to fear of things that are not going to happen. Why are people making mistakes in predictions about Artificial Intelligence and robotics, so that Oren Etzioni, I, and others, need to spend time pushing back on them? Below I outline seven ways of thinking that lead to mistaken predictions about robotics and Artificial Intelligence. We find instances of these ways of thinking in many of the predictions about our AI future. I am going to first list the four such general topic areas of such predictions that I notice, along with a brief assessment of where I think they currently stand. Research on AGI is an attempt to distinguish a thinking entity from current day AI technology such as Machine Learning. Here the idea is that we will build autonomous agents that operate much like beings in the world. This has always been my own motivation for working in robotics and AI, but the recent successes of AI are not at all like this.

Probabilistic models analyze data by relying on a set of assumptions. Data that exhibit deviations from these assumptions can undermine inference and prediction quality. Robust models offer protection against mismatch between a model's assumptions and reality. We propose a way to systematically detect and mitigate mismatch of a large class of probabilistic models. The idea is to raise the likelihood of each observation to a weight and then to infer both the latent variables and the weights from data. Inferring the weights allows a model to identify observations that match its assumptions and down-weight others. This enables robust inference and improves predictive accuracy. We study four different forms of mismatch with reality, ranging from missing latent groups to structure misspecification. A Poisson factorization analysis of the Movielens 1M dataset shows the benefits of this approach in a practical scenario.

Halt and Catch Fire Donna (Kerry Bishe) makes a play to land a heavy hitter, while her ex (Scoot McNairy), has a talk with one of their daughters about a troubling issue at school. Oprah's Master Class In the first of two new episodes, Usher describes how he dealt with fame at a young age and the role that family has played in shaping his career and life. Then soul singer Gladys Knight reflects on her coming of age in the music business, and the reality of touring in the segregated South of the 1950s. By a coincidence in programming all five movies in the action franchise are airing on three channels, but not in order. Up first is the fourth film, "The Bourne Legacy" (2012), starring Jeremy Renner and Rachel Weisz. 3 p.m. FX.

It was great catching up with Ericsson last week in San Francisco at the inaugural Mobile World Congress Americas conference. Ericsson is doing incredible work to advance innovation by partnering with operators globally around IoT and 5G deployments, ranging from testing new radio technology, like advanced MIMO, to new core 5G systems for providing network slicing, to applications like Autonomous vehicles. Ericsson's radio access network was also featured at Sprint's booth where the first 2.5 GHz Massive MIMO field tests were conducted using Sprint's spectrum and Ericsson's radios reaching peak speeds of more than 300 Mbps using a single 20 MHz channel! A great new use case for 5G was intelligent video streaming with Verizon for security and smart city applications, with streams coming to a central, video optimized repository in the core of the 5G network. This 5G overlay to an existing 4G network will provide benefits across multiple applications at the edge of the network from video cameras to drones to industrial control endpoints.

USA Today contributor Jennifer Jolly shows us some absolutely genius're-uses' for an that old iPhone that you have lying around the house. The classic View-Master takes a modern twist, now compatible with your iPhone. The new iPhones are almost here, which means that the older iPhone in your pocket is probably not looking as fresh and fun as it once did. But just because you're jonesing for the hottest new tech doesn't mean yesterday's iPhone can't still make your life better in a totally new way. Here are some genius ways to squeeze a bit more life out of that dated device. It's not just iPhones on Apple's fall release schedule; the HomePod, a Siri-equipped smart speaker is also on the way to compete with the Amazon Echo and Alexa virtual assistant.

Indie developer The Chinese Room is releasing its first-ever virtual reality game. So Let Us Melt is a sci-fi parable about a machine lost in a paradise of its own making. Exclusively available on Google's Daydream VR platform, the title sees the developer reuniting with Bafta award-winning composer Jessica Curry. The game is split into several chapters, each around five to seven minutes in duration. The Chinese Room describes it as an "interactive animated film" with simple controls, making it an ideal entry point for those new to VR. Players assume the role of Custodian 98, a sentient machine that tends to a utopia known as Kenopsia: An environment built to accommodate cryogenically frozen humans upon their awakening.