Sentence examples for Machine learning architecture from inspiring English sources

This bio-inspired machine learning architecture for dictionary learning and sparse classification permits fewer costly physical strain sensors while being simultaneously robust to sensor noise.

We pass the entire multi-band galaxy image into the machine learning architecture to obtain a redshift estimate that is competitive, in terms of the measured point prediction metrics, with the best existing standard machine learning techniques.

If Tesla were going to bet the future of their machine learning architecture on anybody's projections, he would be the person to ask.

That's why companies should invest early in machine learning architecture that continually collects new data and uses it to regularly update its AI models.

From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together.

By highlighting the "projections of what [Tesla's neural networks] would look like into the future", and "our willingness to totally commit to that style of computing", Bannon might be indicating that they have designed their chip for a very specific type of machine learning architecture.

We use different machine learning architectures in order to investigate the accuracy and performance within the case study.

Existing human action classification systems use machine learning architectures and low-level features to characterize a specific human action.

As I became more familiar with the topic, I also spent some time reading research papers and articles on machine learning architectures and algorithms not covered by Andrew's courses.

However, existing machine learning architectures often lack restructuring capability, which is an important process of forming the conceptual structures in human-level synthetic intelligence.

However we do note that Deep Neural Networks require many orders of magnitude more computing resources than standard machine learning architectures, and as such are only tractable for making predictions on datasets of size ≤50k before implementing parallelisation techniques.