Convolutional Neural Networks ---- Off the shelf top notch - PowerPoint PPT Presentation

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Transfer Learning with Convolutional Neural Networks ---- Off the shelf top notch performances Convolutional Neural Networks A breakthough Convolutional Neural Networks VGG-16 example Layers of Convolutional filters Bottleneck

Transfer Learning with Convolutional Neural Networks ---- Off the shelf top notch performances
Convolutional Neural Networks A breakthough
Convolutional Neural Networks VGG-16 example • Layers of Convolutional “filters” • Bottleneck architecture • Magical ? Classification Features extraction
CNNs – Inner working
CNNs – Feature Extraction
Training CNNs • Big Dataset of 100 thousands images – usually millions • Labelled data  Takes time to build • Try out many different Network architecture • Hyperparameter value : – training method, rate – Weights initial values • No feature engineering
Training CNNs – Live view
ImageNet Database
From: AN ANALYSIS OF DEEP NEURAL NETWORK MODELS FOR PRACTICAL APPLICATIONS Alfredo Canziani & Eugenio Culurciello & Adam Paszke
Trained with ImageNet
• Less likely to overtrain
Alternatives • CNN architecture & hyperparameter “automatic” tuning – AutoML (Architecture) – driven by “AI” – IBM Watson Suite (Hyperpameters) – Microsoft Custom Vision Services • Drawbacks – Limited exploration of the space of possibilities – Black box inside a “black API”

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