This article delves into the latest insights and methods for training deep learning models with minimal data. Traditionally, deep learning models thrive on large datasets for optimal performance.

We have written much about large-scale deep learning implementations over the last couple of years, but one question that is being posed with increasing frequency is how these workloads (training in ...

Deep learning is rapidly ‘eating ... most programmers are not able to successfully modify machine-learning systems without substantial specialized training. Even highly trained data scientists ...

However, training deep learning models requires a great deal of computing power. Another drawback to deep learning is the difficulty of interpreting deep learning models. The defining ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More “Deep learning” has become a hot topic in the general rush to ...

"[its] training times are about 7-10 times faster, and... memory footprints are 2-4 times smaller" than those of previous large-scale deep learning techniques. In describing the scale of extreme ...

When it comes to training deep learning models, NVIDIA may be getting all the attention. However, Intel is not sitting quietly, just staring at the massive AI opportunity. It is moving fast in ...