How to develop deep learning models in edge devices? Here is the answer
On-device machine learning uses a simplified version of cloud-based machine learning.
It works by using a model to embed the search query into a high-dimensional vector representing the semantic meaning of the query.
Representation learning is a machine learning (ML) method that trains a model to discover prominent features. It may apply to a wide range of downstream tasks– including Natural Language Processing (BERT and ALBERT) and picture analysis and classification (Inception layers and SimCLR). Last year, researchers developed a baseline for comparing speech representations and a new, […]
Five years ago, Google launched the open-source platform TensorFlow to accelerate machine learning research and empower developers to build AI applications. On the second day of the annual developer conference Google I/O, the technology company announced the latest developments in ML. The keynote session ‘What’s New in Machine Learning’ was hosted by Kemal El Moujahid, […]
Recently, in an official announcement, Google launched an OpenCL-based mobile GPU inference engine for Android. The tech giant claims that the inference engine offers up to ~2x speedup over the OpenGL backend on neural networks which include enough workload for the GPU. This GPU inference engine is currently made available in the latest version of […]
The TensorFlow Team at Google AI has been tirelessly researching on making enhancements and updates to its popular machine learning platform, TensorFlow. The developers at the tech giant have now released the upgraded version of this platform, TensorFlow 2.2.0. TensorFlow 2.2.0 includes multiple numbers of changes and bug fixes in order to make the library […]
The TensorFlow team at Google recently introduced a new tool for TensorFlow Lite (TFLite) known as Model Maker. The TFLite Model Maker simplifies the process of adapting and converting a TensorFlow Neural Network model to particular input data when deploying this model for on-device ML applications. Developed by researchers and engineers from the Google Brain […]
Deep Learning has made several breakthroughs in recent years. Compared to traditional computation platforms, it has become more sophisticated and advanced than ever. Smart homes, intelligent personal assistants, etc. are some of the major breakthroughs in the present era. In this article, we list down 8 platforms that can be used to build mobile deep […]
Tensorflow is amazing when it comes to simplicity in building and deploying machine learning applications in production. And when it comes to mobile devices Tensorflolw’s own Lite version totally saves the day. Tensorflow Lite has already marked its dominance in ML on smartphones in a generation where the entire world depends on handheld devices. In […]
TensorFlow’s machine learning platform has a comprehensive, flexible ecosystem of tools, libraries and community resources. This lets researchers push the state-of-the-art developments in ML and developers easily build and deploy ML-powered applications. At the recently concluded TensorFlow’s developer summit, along with TensorFlow 2.0, the team also announced open sourcing of TensorFlow Lite for mobile devices […]