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Course Outline
Installation for Government Use
- Docker
- Ubuntu
- RHEL / CentOS / Fedora installation
- Windows
Caffe Overview for Government Operations
- Nets, Layers, and Blobs: the fundamental components of a Caffe model.
- Forward / Backward: essential computations in layered compositional models.
- Loss: defining the task to be learned through loss functions.
- Solver: coordinating the optimization process for model training.
- Layer Catalogue: a comprehensive list of layers supporting state-of-the-art models, serving as the basic unit of modeling and computation.
- Interfaces: Caffe supports command line, Python, and MATLAB interfaces for flexibility and integration.
- Data Preparation: guidelines on preparing data for model input in Caffe.
- Caffeinated Convolution: an explanation of how Caffe performs convolution operations efficiently.
New Models and Code for Government Applications
- Detection with Fast R-CNN
- Sequence Modeling with LSTMs and Vision + Language Integration with LRCN
- Pixelwise Prediction with Fully Convolutional Networks (FCNs)
- Framework Design and Future Directions for Government Use
Examples for Government Training
- MNIST: a foundational dataset for training and testing machine learning models.
Requirements
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21 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
