In this article, we have discussed Model Search, a flexible and domain agnostic TensorFlow framework for automated ML
When it comes to Deep Neural Network (DNNs), we are often confused about their architecture(like types of layers, number of layers, type of optimization, etc.) for a specific problem. This sudden template shift of using deep learning models for a various number of problems has made it even harder for researchers to design a new neural network and generalize it. In recent years, automated ML or AutoML has really helped researchers and developers to create high quality deep learning models without human intervention and to extend its usability, Google has developed a new framework called Model Search.
Model Search is an open-source, TensorFlow based python framework for building AutoML algorithms at a large scale. This framework allows :
A researcher found that phone numbers tied to WhatsApp accounts are indexed publicly on Google Search creating what he claims is a “privacy issue” for users.
Google Cloud Dataflow is a fully-managed service for executing Apache Beam pipelines within the Google Cloud Platform(GCP). In a recent blog post, Google announced a new, more services-based architecture called Runner v2 to Dataflow – which will include multi-language support for all of its language SDKs.
Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.
In this post, we will show how to perform hyper-parameter search using an automated machine learning (AutoML) tool — NNI (for Neural Network Intelligence) open-sourced by Microsoft.
Detector-Classifier Neural Network Architecture with TensorFlow. We’re gonna go over the training of an object detection model, a detector, with TensorFlow Object Detection API and using that model to extract data for our classification model.