Machine learning works on the basis of large, historical datasets that have been created using a collection of data across many clients and industries. Even companies that only process a relatively small number of transactions are able to take full advantage of the data sets for their vertical, allowing them to get accurate decisions on each transaction. This aggregation of data provides a highly accurate set of training data, and the access to this information allows businesses to choose the right model to optimize the levels of recall and precision that they provide: out of all the transactions the model predicts to be fraudulent (recall), what proportion of these actually are (precision)? Once the accuracy of the models is deemed acceptable it is time to start predicting, but where do such predictions come from?
AppBrain statistics show that Cordova, with all its limitations, is persistently used in development of complex apps for quality-demanding industries. In other words, developers are simply making fish climb trees, as they use Cordova for projects it wasn’t meant for from the very start. While Cordova takes up only 1.40% of U.S. top-500 apps, its installs are even lower - mere 0.49% - which only proves that the results of the developers’ attempts aren’t satisfying the users in the least. The low adoption figure also hints at the fact that most of complex apps created with Cordova turn out to be unsuccessful and disappoint users, who quickly abandon them due to underperformance. Then, most likely, developers blame it all on the tool instead of accepting that they asked too much from it.
It doesn't matter how much content you create and share; if you don't have a tribe of your own, that content of yours is just never going to get seen. You can't rely on organic audience growth, either, not unless you're already famous. You have to build your own following. You have to find people interested in your area of expertise, in the niche in which you want to become an influencer, and connect with them. If you're building a Twitter following, follow people in your target audience so that if your profile and content resonate, they will follow you back. The same goes for Facebook, Instagram, and Linkedin. What's more, you have to do all this on a daily basis. You have to be relentless about building your following until you get to that point where organic growth kicks in; and even then, I would continue building it.
The term “digital transformation” has a nice ring to it, but few organizations understand the true meaning of the words. Most believe it simply refers to moving away from inefficient, outdated technologies. However, the textbook definition of digital transformation necessitates a fundamental shift or evolution of your business model, changing the very way in which your business is conducted. Few organizations complete full-fledged digital transformations. For instance, updating an outdated mainframe system to a modern, service-oriented architecture in the cloud is not a digital transformation. A brick-and-mortar bank shifting its focus to an electronic online presence with new products and services is. It’s important to acknowledge that while many organizations either believe that they need a digital transformation or are in the midst one, few actually requirea large business transformation.
For the selection of business intelligence tools, one can browse the CVs of their employees for some experience in the field of BI. These employees can help them evaluate BI products. It is easier to have a software which even employees with basic skills can operate, ease of work and less money on training more people! ... Choosing a software according to company requirements is the priority. Many BI providers have different versions of software packages. These versions are priced according to the features enabled within each package. Hence, It is wise to decide on basis of prime requirements of the software. It is not necessary that a software with more advanced features is always useful. You might end up with more capital cost to the company as well as an increase in maintenance cost due to increase in hardware.
In the last 10 years, neural networks have made a huge leap in growth. Today they are applied in a wide range of applications and are gradually replacing traditional ML methods. I’d like to show you how the deep learning approach is used by YouTube. Undoubtedly, it’s a very challenging task to make recommendations for such a service because of the big scale, dynamic corpus, and a variety of unobservable external factors. According to the study “Deep Neural Networks for YouTube Recommendations”, the YouTube recommendation system algorithm consists of two neural networks: one for candidate generation and one for ranking. In case you don’t have enough time, I’ll leave a quick summary of this research here. Taking events from a user’s history as input, the candidate generation network significantly decreases the amount of videos and makes a group of the most relevant ones from a large corpus.
Since IoT technology connects the internet with objects that are ubiquitous in our daily lives, marketers in almost every industry will be able to engage consumers throughout every phase of the customer journey. The term “Big Data” is an understatement for the amount of data IoT devices will produce. According to the Ericsson Mobility Report, IoT devices and sensors will exceed mobile phones as the largest category of connected devices in 2018 and generate a staggering 400 zettabytes of data per year. IoT's surge will overjoy marketers because they can leverage these massive data sets to integrate consumer behavioral signals into their marketing stack. This will allow them to capture interactions, conversion metrics, and consumer behavior predictions and link them to purchase-intent data.
According to the State of Work Productivity Report, 65% of full-time employees think a remote work schedule would increase productivity. This is backed up by more than two-thirds of managers reporting an increase in overall productivity from their remote employees. Where do telecommuters find this extra boost of productivity? With none of the distractions from a traditional office setting, telecommuting drives up employee efficiency. It allows workers retain more of their time in the day and adjust to their personal mental and physical well-being needs that optimize productivity. Removing something as simple as a twenty minute commute to work can make all world of difference. If you are ill, telecommuting allows one to recover faster without being forced to be in the office. It also improves the impact on our overall health.
The first big conceptual leap that we have to make is to understand that learning systems evolve in non-equilibrium settings. ... Stated in a different way, researchers should be very cautious about employing statistical or alternatively bulk thermodynamic metrics in their analysis of these systems. It is my belief that one of the most glaring inappropriate tools in the study of AI is the use of Bayesian methods. ... The second conceptual leap is to understand that our of what “Generalization” means is quite grossly inadequate. The use of the term in Machine Learning is extremely liberal. Furthermore, the Machine Learning approach of ‘curve fitting’ and thus interpolation and therefore generalization between adjacent points in the fitted curve, breaks down under the recently discovered notion of rote memorization of Deep Learning.
Teams today are adopting cloud services that reduces the pain of managing a local device/desktop browser labs. In addition, teams are either developing overlays on top of the open-source frameworks like Appium/Protractor and such to close test automation coverage issues, or are using a mix of tools as part of their tool stack to get proper testing capabilities. In addition, keeping an eye on analytics and market trends as a way to understand how the market is moving, and which devices are trending up or down, can also help. With analytics in mind, having a well structured testing strategy that learns from previous executions and provides insights back to the team can help focus the testing on the most valuable tests - quality and value over quantity. As an example, identify the tests that found the most defects per platform.
Quote for the day:
"Leaders keep their eyes on the horizon, not just on the bottom line." -- Warren G. Bennis