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.
Mosaic helped a North American provider of international freight forwarding services build an NLP solution that automates the tariff classification process, saving valuable resources and reducing risk while improving the customer experience.
Mosaic dives into our latest solution framework, The Neural Search Engine. Our tool can be tuned to ingest any set of documents and return relevant information in seconds. We leverage the retriever/reader architecture where for a given question, related context is obtained from the enterprise-specific knowledge base and fed to deep learning models.
Mosaic helped a commercial airline integrate custom machine learning to predict departure runway and taxi-out time, cut operational costs, and improve the customer experience, aiding in reducing fuel emissions with AI.
Mosaic worked with a vet tech company to develop an encompassing pet wellness score and early disease-identification models using an AI-driven collar device that collects pet behavioral information and reports health insights to the owner’s app using IoT and sensor data techniques.
Wielding the power of computer vision human action extraction to gain insights from video data presents an opportunity for logistics companies to make improvements for new and current customers.
Fantasy sports represent a rich and exciting world of modeling and analytic possibilities. With the advent of modern computer vision, statistics tracking, and the general embrace of the sporting community of a “data-centric” view to the game, there is a wealth of information available about each player, their performances, and various metadata.
In our whitepaper, Mosaic explores deep learning , including when to use deep learning over machine learning using practical examples.
Anytime you wish to predict the transient state(s) of something or someone constantly monitored by sensors, time series classifications are the right tool. This article will explain some basic concepts of using deep learning models for TSC and finish with a brief discussion of ways to improve the performance to save on cost and speed.
Filtering search results is an essential part of any eCommerce website. Can you remember the last time you shopped online without filtering on product attributes such as color, size, brand, etc. Additionally, rich product attributes are critical to Google SEO which drives traffic and website sales.
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.