Session 1.3: K means Clustering in OpenCV Session 1.4: Reverse a Video Session 3.2: Create negative images Session 3.3: Create positive images

Before doing any data analysis, the need to find out any outliers in a dataset arises. These outliers are known as anomalies. This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring. The main goal of Anomaly Detection analysis is to identify the observations that do not adhere to general patterns considered as normal behavior. For instance, Figure 1 shows anomalies in the classification and regression problems.

In this project, we'll cover how to work with Cat and Dog Images and feed them to a machine learning classifier in chunks also known as "batch sizes" in Keras. Using this we'll evaluate the efficiency with and without using this and also cover the basics of how Machine Learning works and how to feed data to the model.

Wei Li is vice president in the Software and Services Group and general manager of Machine Learning and Translation at Intel Corporation, responsible for several areas of software systems, including machine learning, binary translation, and emulation. His team works with industry and academia to enable the software ecosystem, and collaborates with Intel hardware teams designing future processor products. Since joining Intel in 1998, Wei has led teams that contributed to Intel data center, client/mobile, Internet of Things, and artificial intelligence businesses. He holds 11 U.S. patents, and has served as an associate editor for ACM Transactions on Programming Languages and Systems. Wei earned a Ph.D. in computer science from Cornell University, completed the Executive Accelerator Program at the Stanford Graduate School of Business, and he taught computer science at Stanford University.