This tutorial, led by Evan Shelhamer, Ph.D. student at UC Berkeley and lead developer of the Caffe deep learning framework, is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe – Convolutional Architecture for Fast Feature Embedding – framework that offers an open-source library, public reference models, and worked examples for deep learning. Join the tour from the 1989 LeNet for digit recognition to today's top ILSVRC2014 vision models. This tutorial focuses on vision, but includes coverage of general techniques and tools.