White Papers
How Deep Learning Facilitates Automation & Innovation and When to Use It
In our whitepaper, Mosaic explores deep learning , including when to use deep learning over machine learning using practical examples.
Mosaic leverages innovative ML tools and techniques to drive innovation across the Life Sciences industry. Mosaic has specific expertise in optimizing the production & supply chain processes, improving trial forecasting & operations, leveraging innovative graph neural-network architectures for drug discovery and precision medicine, deploying deep learning to IoT streams to drive critical insights, and creative data-driven approaches to legacy trend forecasting & sales/marketing applications.
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.
Text data presents a tremendous opportunity to benefit all stakeholders of an organization – investors, employees, processes, and the all-important customer – if the organization can find a way to sift through this data in an automated way to extract key information and solve specific challenges. In that case, they could learn about their firm and start optimizing the way they operate.
Deep learning, specifically computer vision and natural language processing, can be designed to identify defects during the product packaging process. These deep learning models can verify that a label on a package is present, correct, straight, and readable.
Successfully developing machine learning tools to provide value in a business environment requires much more than algorithmic knowledge and tuning. Machine learning cannot provide value in a vacuum.
Global external shocks are going to continue to happen, that is a fact of operating a business in today’s environment. As companies embrace data science in their decision-making processes, they are better positioned to deal with these disruptions, allowing them to manage a risk-optimized supply chain.
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Designing and deploying computer vision is a powerful technology that humans can employ to improve their decision making. The only limits to these technologies lie within our ability to think of problems for them to solve.
Mosaic writes an in-depth view on the synergies between design thinking & data science. We also examine how these two disciplines can be used together.
We designed and deployed a custom NLP engine to facilitate better population health decisions for the CDC.