You may not be sure why your coffee pot should talk to your toaster, but precision technology powering an Internet of Things has the potential to reshape our planet. To help clarify, Dr. Timothy Chou (Lecturer at Stanford University) created Precision to introduce us to the basics of the Internet of Things, with a focus on business solutions. The first part of Precision Industries introduces a vendor-neutral, acronym-free, five layer framework to help us better understand the Internet of Things. The module then dives into each layer of the framework in more detail: You learn about Things (after all we are talking about the "Internet of Things"), how they Connect to the network, how to Collect data coming from these networked machines, what can be done to Learn from this data, and, finally, what you can Do differently given learned insights from deployed IoT solutions. The course highlights both fundamental Principles, as well as many real-world examples put into Practice.
Innovation is critical to the success of any organization. In the enterprise, the introduction and development of enterprise data layers are instrumental in driving innovation and business agility, but not in the way some people think. You can't expect to come up with a single product, no matter how amazing it is, and have it stick around forever. There's always someone working on trying to do it better, and there's always someone trying to build something that makes you irrelevant – or at the very least, outdated. That being the case, forward-thinking enterprises need to do everything they can to encourage innovation throughout the organization.
Security researchers have recently observed a large application-layer distributed denial-of-service attack using a new technique that could foil DDoS defenses and be a sign of things to come for Web application operators. The attack, which targeted a Chinese lottery website that used DDoS protection services from Imperva, peaked at 8.7Gbps. In a time when DDoS attacks frequently pass the 100Gbps mark, 8.7Gbps might not seem much, but it's actually unprecedented for application-layer attacks. DDoS attacks target either the network layer or the application layer. With network-layer attacks, the goal is to send malicious packets over different network protocols in order to consume all of the target's available bandwidth, essentially clogging its Internet pipes.
For a better approach to make your company smarter, look to APIs. IBM Watson), you can create smarter systems that provide top notch analysis without the need of a data lake. APIs enable data to flow into the machine learning system (since all interactions with the application happens through the APIs, the API traffic represents the totality of all signals about an app's usage). But more importantly, APIs represent a very simple way of taking action. Recommendations can be pushed, for example, as part of the API responses.
Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. We believe these benchmarks provide an easy and efficient way to conduct reproducible experiments for neural hyperparameter search. Our benchmarks consist of a large grid of configurations of a feed forward neural network on four different regression datasets including architectural hyperparameters and hyperparameters concerning the training pipeline. Based on this data, we performed an in-depth analysis to gain a better understanding of the properties of the optimization problem, as well as of the importance of different types of hyperparameters. Second, we exhaustively compared various different state-of-the-art methods from the hyperparameter optimization literature on these benchmarks in terms of performance and robustness.