Artificial Intelligence Future

Introducing TensorFlow Privacy: Learning with different privacy for training data Today, we are excited to announce TensorFlow Privacy (GitHub), an open-source library that makes it easier for developers to not only train machine-learning models with privacy but also to advance the state of the art with machine learning. Strict privacy guarantee. Modern machine learning is increasingly used to create amazing new technologies and user experiences, many of which involve training machines to learn responsibility from sensitive data, such as personal photos or emails. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples. To ensure this, and to give strict privacy guarantees if the training data is sensitive, it is possible to use technology based on different privacy principles. In particular, when trained in user data, those technologies offer strict mathematical guarantees that the model wil

TensorFlow Item Search API, ML Engine, and Swift Note: As of this writing there is no official TensorFlow library for Swift, I used Swift to build client applications for predictive requests against my model. That may change in the future, but Taylor has the final say. The TensorFlow Object Detection APO demo helps you identify the location of objects in the image which can lead to some super cool applications. But because I spend more time taking photos of people, rather than things, I want to see if the same technique can be applied to identify faces. Turns out it worked well! I used it to build the Taylor Swift detector in the picture above. In this post I will outline the steps to take the T-Swift images from the iOS app which predicts against the trained model: Pre flow streams: Resize, label, split them into training and test sets, and convert to Pascal VOC format Convert images to TFRecords to be fed into the Item Search API Train the model on the Cloud ML engine using MobileNet

Using tf.Press () in TensorFlow I know you will definitely use debuggers correctly, every time, and will never use print statements to debug your code. Right? Because if you did, you might find that TensorFlow's print statement doesn't work like normal print statements. Today I'm going to show you how TenserFlow's print statement works, and how to make the most of it, hopefully saving you some confusion along the way. Printing in TensorFlow There are a few ways to print things out when writing the TensorFlow code. Of course, there is the classic Python built-in, print (or function print (), if we're going to be Python about it). And then there's TensorFlow's print function, TFprint (note the capital P). When working with TensorFlow, it should not be forgotten that everything is ultimately a graph calculation. This means that if you print a TensorFlow operator using a Python print, it only shows a description of what that operation is because no values ​​have