Deep Learning for Vision with Caffe Training Course

Overview

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

understand Caffe’s structure and deployment mechanisms
carry out installation / production environment / architecture tasks and configuration
assess code quality, perform debugging, monitoring
implement advanced production like training models, implementing layers and logging

Nets, Layers, and Blobs: the anatomy of a Caffe model.
Forward / Backward: the essential computations of layered compositional models.
Loss: the task to be learned is defined by the loss.
Solver: the solver coordinates model optimization.
Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
Interfaces: command line, Python, and MATLAB Caffe.
Data: how to caffeinate data for model input.
Caffeinated Convolution: how Caffe computes convolutions.