Live Master Class

Introduction to Convolution Process of CNN


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About this session

Convolutional Neural Network (CNN) is a type of Deep Neural Network which is majorly used in applications related to Computer Vision such as image classification, image recognition and object detection etc. In general, Convolution process yields a new function by merging two functions. In CNN specifically, the process of convolution takes place on the input data with the help of filters which produces a feature map.

In this session, we will talk about Digital image, the feature of digital image, Convolution process to locate features of image and pooling features such as max and average pooling. We will understand in detail about light wells, pixels, image as numerical grid and function, image features such as pixel gradient as edges etc. We will also uncover kernels and various filters along with feature maps in the Convolution process of CNN.

About the Speaker

Prof. Mukesh Rao


Consultant, Big Data & Machine Learning


Prof. Mukesh Rao is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science.

Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRISP DM, tools and techniques used in each stage, machine learning algorithms and their application. Big Data training covers core Apache Hadoop technologies including HDFS, YARN, Map Reduce, PIG, HIVE, SQOOP, FLUME, SPARK and MongoDB.

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