Deep learning is a subfield of machine learning that focuses on training artificial neural networks to perform tasks that typically require human-like intelligence. Explore our blogs, case studies, and white papers for examples, applications, and ideas surrounding deep learning techniques. Mosaic Data Science has decades of experience applying this technology to various use cases.
We decided to approach this problem as a similarity learning modeling effort. We used convolutional neural networks to train a model that takes an image or video as input and outputs a vector representation of the input, such that similar inputs will be close to each other in the vector space. The vector learning is driven by a triplet loss function.
Object detection in video has become a matter of routine, however, expanding these models to detect an object of your choosing requires many thousands, if not tens of thousands, of training examples. Few shot learners seek to make this process cheaper and easier by learning to detect new objects with only a small handful of examples (i.e. 1-30).
We designed and deployed a custom deep learning based tool to extract images from millions of digital and scanned documents.
One of our data scientists takes an intensive review of deep learning applications, exploring how deep learning is being applied in the world today and what it means at Mosaic.