Hands-On Deep Learning with Caffe2

Practical use cases will teach you to code once and run your Deep Learning models anywhere

Caffe2, open-sourced by Facebook, is a simple, flexible framework for efficient deep learning. This course will teach you about Caffe2 and show you how to train your deep learning models.

The course starts off with the basics of Caffe2 such as blobs, workspaces, operators, and nets; moving on, you will learn how to build a model using Caffe2's new API brew. You will also learn how to create Convolutional Neural Networks (CNNs) that can identify not only handwriting but also fashion items from an image. You will work on transferring learning to allow you to work with CNN's for image recognition by fine-tuning models that are already pre-trained on a large-scale dataset. We cover common models such as ResNet-50. Finally, the course will show you how to deploy your models on any platform.

By the end of this course, you will be able to effectively train Deep Learning models with Caffe2, providing you with high-performance and first-class support for large-scale distributed training, mobile deployment, new hardware support, and flexibility.

All the code files for this course are available on Github at https://github.com/PacktPublishing/Hands-On-Deep-Learning-with-Caffe2

Style and Approach

All classes in this course are hands-on; you will get sufficient background about each class's content, and you will then go through critical examples that you need to know. At the end of each to, you will also be presented with quizzes to help you master Caffe2.

What You Will Learn
Install Caffe2 and prepare your developing environment.
The basic elements of Caffe2—such as blobs, workspaces, and tensors—and how to use them to build a computational graph.
Foundational knowledge about training models using Caffe2.
The brew, an API for creating models in Caffe2.
Address the supervised learning problem of image classification using Caffe2.
How to use RNNs in Caffe2 to learn to write poems like Shakespeare.
Deep Q Network, and how to use it in Caffe2.
Running models on devices with Caffe2.