Unleash Deep Learning Begin Visually with Caffe and DIGITS

An introduction to Deep Learning tools using Caffe and DIGITS where you get to create your own Deep Learning Model

Learn the basics of Deep Learning with hands on exercises using the Caffe deep learning framework and the DIGITS visual interface. Build your own model and start classifying images.

Begin with a visual understanding of machine learning and deep learning concepts with this quick dive tutorial for beginners.

image classification
feedfoward neural network
convolutional neural network
digit recognition


A hot new topic with lots of opportunities

Artificial intelligence, machine learning and deep learning are in the news and all around us. They give us the promise of computers solving tasks that until recently were very hard for computers: speech recognition, translation, object recognition, image classification, autonomous driving cars.

Caffe framework is free, open sourced, continuously improved, has good documentation and even has an entire zoo of pre trained deep neural network models for image classification and other computer vision tasks. It is very fast and extensible and has most layers and utilities one could hope for (convolutions, pooling, relu, softmax, accuracy) so all you have to do is understand how to control this powerful tool.

DIGITS is NVIDIA's tool to help improve the process of designing, debugging and visualizing the inner workings of a deep neural network and works perfectly with Caffe.

The underling idea is very simple: instead of explicitly programming one should give lots of labeled examples and allow the computer to learn.



Content and Overview

Suitable for beginning deep learning engineers.

Thanks to DIGITS and Caffe there is a little programming and a lot of visual steps but a good mathematical and programming background is recommended.

You should already have Ubuntu (recommended) and DIGITS installed (a fork of Caffe will be included with the DIGITS installation).

The course will take you through the natural steps of getting training and testing data, designing a model, training the model and evaluating it.

Students completing the course will have the knowledge and courage to experiment and create amazing, useful and functional Convolutional Deep Learning Networks.