Supervised learning needs labels, or annotations, that tell the algorithm what the right answers are in the training phases of your project. In fact, many of the examples of using MXNet, TensorFlow, and PyTorch start with annotated data sets you can use to explore the various features of those frameworks. Unfortunately, when you move from the examples to application, it's much less common to have a fully annotated set of data at your fingertips. This tutorial will show you how you can use Amazon Mechanical Turk (MTurk) from within your Amazon SageMaker notebook to get annotations for your data set and use them for training. TensorFlow provides an example of using an Estimator to classify irises using a neural network classifier.
Computers may not wear tennis shoes (yet), but thanks to developing artificial intelligence technologies, they're smarter than ever before. Along with those technologies has come a relatively new category of computer science called machine learning, or ML. Similar to statistics, ML involves computer systems that utilize algorithms to automatically learn about data, recognize patterns, and make decisions, all without outside intervention or explicit directions from human beings. In the real world, you can find it being used in smart assistants like Siri and the Amazon Echo, in online fraud detection services, in the facial recognition feature that identifies photos of you on Facebook, and more recently, in Tesla's self-driving car. ML is distinctive in the world of AI in that it can be used to process vast amounts of data quickly, making it a desirable tech skill among job applicants not only in the fields of computer science and engineering, but also marketing, health care, finance, social media, and beyond.
At the beginning of last year we announced an Amazon Polly plugin for WordPress. This plugin allows blog and website creators who are using WordPress to quickly and easily create audio versions of their posts, articles and websites. A few months later, we updated the plugin with the ability to quickly translate the content of websites to other languages using the Amazon Translate service. This functionality, together with the ability to create audio versions, allows you to voice the content of sites in translated languages. We want to allow creators and authors to reach more readers/listeners around the world using the latest AI services offered by AWS.