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Understanding Convolutional Neural Networks and Feature Maps
By Rahul Talari, CEO - Lylt.io presented as part of the AI & ML meetup. Convolutional Neural Networks have taken the software industry by storm into a new phase where learning-based systems are being utilized in production in several verticals including healthcare, advertising, business intelligence etc. In this talk I would like to help you understand what Convolutional Neural Networks are on a high level and dive into the components that help it make the decisions it makes. Since we consider machine learning models usually as black-boxes, I would like to showcase some interesting characteristics of these models that help it make decisions on real-world datasets. Some examples include Saliency Maps, Class Visualization and Image Fooling. Also we will talk about Neural Style Transfer where images can be morphed into different artistic styles.
Understanding Convolutional Neural Networks and Feature Maps
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- 61. Summary - CNNs arein utilized in large scale production use cases - CNNs use layers such as Conv, MaxPool, Fully Connected etc - CNNs can be visualized to study what they are doing - There are several architectures used today such as AlexNet, VGGNet, ResNet etc. - Various methods such as Saliency Maps, Feature Inversion, Style Transfer help understand them
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- 64. Contact: rahulraju93@gmail.com Phone: +917349497007,+16122084881 Thank you!