In my recent blogs, I have written about automation tying the network to other domains of IT, and how it's a capability available today that you should start using. Machine learning is another hot topic. While the timeline is several years out for many machine learning applications in networking, it has the potential to be one of those rare technologies that comes along every few decades and fundamentally transforms how networks run. After all, leading companies such as Amazon, Apple, Facebook, Google and Baidu are already transforming their products and business processes with machine learning. Hopefully, as the technology matures, much of the inner workings of it will be deep inside the systems and clouds of your vendors.

Developing machine learning solutions that give a lift from your existing prediction algorithms is not an easy task. They require a multitude of activities to get it right including cleaning up the data, setting up the infrastructure, testing & re-testing the model & finally deploying the algorithm. Here are five machine learning services that can help reduce the pain of deploying your machine learning solution. Based on Microsoft's Azure Cloud Platform, Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments. Machine Learning Studio features a library of time-saving sample experiments, R and Python packages and best-in-class algorithms from Microsoft businesses like Xbox and Bing. Azure ML also supports R and Python custom code, which can be dropped directly into your workspace.

We talk about artificial intelligence (AI), robots, and machine learning as if they're coming soon, or are just some tech pipe dream. In fact, a special report from Bank of America, Merrill Lynch predicts the global market for AI and robots will be just under $153 billion by 2020, and some industries will experience up to a 30% productivity increase through the use of those technologies alone. That can either terrify you if you've seen too many sci-fi films, or excite you if you consider the upside and benefits it could yield. The reality probably lies somewhere in the middle. There will be disruption – there will be jobs and perhaps even whole industries that see massive displacement from robots and other "intelligent" machines. And that says nothing of the inherent risk associated with creating something capable of logical thinking without emotion. The robots may not rise up and exterminate humanity any time soon, but the development of true AI is closer than you think.

Teen scientists use machine learning and neural networks to detect and diagnose diseases, track space debris, design drones and justify conclusions at Intel ISEF 2017. While sentient computer beings like HAL from the classic 2001: A Space Odyssey or Samantha from the 2013 film Her may still be on the distant horizon, some forms of artificial intelligence (AI) are already improving lives. At the 2017 Intel International Science and Engineering Fair (ISEF) – where nearly 1,800 high school students gathered to present original research and compete for more than $4 million in prizes – the next generation of scientists used machine learning and artificial neural networks to find solutions to some of today's most vexing problems. "AI is critical to our future," said Christopher Kang, a budding computer scientist from Richland, Washington, who won an ISEF award in the robotics and intelligent machines category. "Humans have a limit as to how much data we can analyze," he said.

This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones. The proposed UL layers can play two roles: they can be the cost function layer for providing global training signal; meanwhile they can be added to any regular neural network layers for providing local training signals and combined with the training signals backpropagated from upper layers for extracting both slow and fast changing features at layers of different depths. Therefore, the UL layers can be used in either pure unsupervised or semi-supervised settings. Both a closed-form solution and an online learning algorithm for two UL layers are provided. Experiments with unlabeled synthetic and real-world videos demonstrated that the neural networks equipped with UL layers and trained with the proposed online learning algorithm can extract shape and motion information from video sequences of moving objects. The experiments demonstrated the potential applications of UL layers and online learning algorithm to head orientation estimation and moving object localization.

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

A new report predicts that artificial intelligence (AI) in the U.S. education sector will grow 47.5 percent through 2021. The report, Artificial Intelligence Market in the U.S. Education Sector 2017-2021, is based on in-depth market analysis with inputs from industry experts. One of the major trends surrounding AI and education is AI-powered educational games. Because games have the potential to engage students while teaching them challenging education concepts in an engaging manner, vendors are incorporating AI features into games to enhance their interactivity. Educational games that include adaptive learning features give students frequent and timely suggestions for a guided learning experience.

Includes key concepts of statistical analysis - Probability theory, Types of distribution, Central limit theorem, Hypothesis testing, Statsistical inference.

Welcome to Essential Education, our daily look at education in California and beyond. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today. California State University's Board of Trustees are meeting Tuesday and Wednesday to discuss graduation rates, executive compensation and the budget shortfall. The L.A. Unified Board of Education's curriculum and special education committees are also meeting today.

Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We show that for simple overparameterized problems, adaptive methods often find drastically different solutions than gradient descent (GD) or stochastic gradient descent (SGD). We construct an illustrative binary classification problem where the data is linearly separable, GD and SGD achieve zero test error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to half. We additionally study the empirical generalization capability of adaptive methods on several state-of-the-art deep learning models. We observe that the solutions found by adaptive methods generalize worse (often significantly worse) than SGD, even when these solutions have better training performance. These results suggest that practitioners should reconsider the use of adaptive methods to train neural networks.