Demystifying Neural Networks
Neural Networks are at the cutting edge of Machine Learning, and Artificial Intelligence. Often the black-box reference made to explain NN's leads to folks with little experience in Machine Learning, statistics or big data to become intimidated. There is a powerful synergy between DevOps and Neural Networks, NN's that use automatic feature extraction give exponential leverage in the processing of monitoring production data, identifying patterns or anomalies by analyzing application log events, and verifying production deployments.
In this talk I will walk you through how we can "Demystify Neural Nets". By utilizing open-source libraries like TensorFlow, and Keras, I will demonstrate how one can build a neural network in no time. The specific focus for this presentation will be Long Short-term Memory neural networks (LSTM), that are extremely valuable in building models for time-series data like system metrics data, and application log data. This presentation is geared towards folks interested in the field of ML, both beginners and intermediate levels.
Boshika has four plus years of experience working as a full-stack engineer in San Francisco Bay Area and Los Angeles. She currently works for Commercial Tech at Capital One, where she is building microservices in Golang for commercial document migration. She is also part of the agenda at Commercial Tech to apply machine learning to solve business cases related to data classification, and data extraction. Boshika became fascinated by the field of data science and machine learning while doing bench research at Stanford University, where she was using ML algorithms to analyze large scale genome sequencing data. She is also currently pursuing her Master’s in Data Science at Johns Hopkins University.