Graham Ganssle, Ph.D., P.G.

Increasing the accuracy of analytics technologies drives modern business, and getting more realistic results applicable to on-the-ground business operational conditions means orgs can make better decisions faster.

Advances in automation of data analysis, graph analytics, data lineage, conversational queries and IoT analytics are widely predicted for 2020. Expero has capabilities and assets already in place.

Deep dimensionality exchange is a new deep learning protocol developed at Expero for performing domain transfer between domains of different dimensions.

Data products are productized versions of data science and machine learning initiatives that deliver value to end-users.

Graph machine learning (graphML) is a subset of deep learning with much higher accuracy because big data records are linked together by their relationships.

#NLP #MachineLearning #algorithm learns to tell #stories by summarizing #commercial #RealEstate #data, #earning #profits and spurring #CustomerRetention. #BINGO!

Deep learning, specifically recurrent neural networks, forecast nonlinear time series illness signals.

Using deep machine learning recommender system technology, Expero detects health care opioid fraud.

Reinforcement Learning of a deep neural network has been applied to the problem of supply chain logistics: In a stochastic environment, how to optimize pickup and delivery schedules.

Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.

Money laundering and credit card fraud detection by graph machine learning.

Graph convolutional networks exhibit optimal deep learning on big graph data to gain business insight.

Machine learning entity resolution deduplication of FBI criminal records using supervised learning logistic regression and unsupervised learning clustering.

Deep-learning convolutional generative adversarial neural network crushes web design.