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.
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Understanding Convolutional Neural Networks and Feature Maps
- 9. Diving In...
- 60. Neural Style Transfer Copyrights@CS231N, Stanford
- 61. Summary - CNNs are in 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
- 63. Questions?
- 64. Contact: rahulraju93@gmail.com Phone: +917349497007, +16122084881 Thank you!