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Getting Started with Caffe - Deep Learning Framework Introduction - Class 3
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- 1 Introduction
- 2 Agenda
- 3 What is Caffe
- 4 What does Caffe do
- 5 How does Caffe work
- 6 Data preprocessing
- 7 Defining a deep neural network
- 8 Defining loss functions
- 9 Training your network
- 10 Output from Caffe
- 11 Binary model files
- 12 Model Zoo
- 13 Localization
- 14 Pixel Level Classification
- 15 Sequence Learning
- 16 Transfer Learning
- 17 GPU acceleration
- 18 QDNNI
- 19 Jetson TK1
- 20 Handson lab preview
- 21 Getting started with Caffe lab
- 22 Questions
- 23 Tesla GPUs
- 24 Which frameworks are most popular
- 25 Can I use Caffe without the GPU
- 26 Model file compatibility
- 27 QDNN requirements
- 28 Multiple GPUs
- 29 Depth Images
- 30 Giraffes vs Horses
- 31 Embedded Deployment
- 32 Continuous Learning
- 33 Inline Comments
- 34 Where can I learn how to format the database
- 35 What does LevelDB mean
- 36 What does LnDB mean
- 37 What does Batch Size mean
- 38 How many iterations should I use
- 39 Can I use the C API
- 40 Endtoend deep learning
- 41 Multivariate regression
- 42 Defining custom layers
- 43 Sensor fusion
- 44 Read images from OpenCV
- 45 Caffe models
