Explore the fundamentals of Caffe, a powerful Deep Learning framework, in this comprehensive video course. Learn about Caffe's program structure, core functionality, and integrated GPU acceleration. Discover how to manage data, define and train Deep Neural Networks (DNNs), select training parameters, monitor progress, and deploy models for classification or feature extraction. Gain practical experience through hands-on examples and explore advanced topics such as localization, pixel-level classification, sequence learning, and transfer learning. Understand GPU acceleration, QDNN, and deployment on platforms like Jetson TK1. Dive into popular frameworks, multi-GPU setups, embedded deployment, continuous learning, and custom layer definitions. Master essential concepts like batch size, iterations, and database formatting while exploring Caffe's potential for end-to-end deep learning, multivariate regression, and sensor fusion applications.
Getting Started with Caffe - Deep Learning Framework Introduction - Class 3
Overview
Syllabus
Introduction
Agenda
What is Caffe
What does Caffe do
How does Caffe work
Data preprocessing
Defining a deep neural network
Defining loss functions
Training your network
Output from Caffe
Binary model files
Model Zoo
Localization
Pixel Level Classification
Sequence Learning
Transfer Learning
GPU acceleration
QDNNI
Jetson TK1
Handson lab preview
Getting started with Caffe lab
Questions
Tesla GPUs
Which frameworks are most popular
Can I use Caffe without the GPU
Model file compatibility
QDNN requirements
Multiple GPUs
Depth Images
Giraffes vs Horses
Embedded Deployment
Continuous Learning
Inline Comments
Where can I learn how to format the database
What does LevelDB mean
What does LnDB mean
What does Batch Size mean
How many iterations should I use
Can I use the C API
Endtoend deep learning
Multivariate regression
Defining custom layers
Sensor fusion
Read images from OpenCV
Caffe models
Taught by
NVIDIA Developer
