The MyCaffe-Samples include numerous sample applications that show how to use MyCaffe and are organized under the following topics:

BlobUsage demonstrates how to load and manipulate blobs of data stored in GPU memory.

CudaDnn/CudaDnnTest demonstrates how to use GPU memory, including memory pointers.

ImageClassification demonstrates various ways of loading image data and training simple image classification problems.

MemoryDataLayer demonstrates how to use the MemoryDataLayer.

OneShotLearning demonstrates how to perform one-shot learning on small datasets using the Siamese network. A separate sample demonstrates how to train these models using Python.

OnnxExamples demonstrates how to use the OnnxControl to load ONNX model files.

ReinforcementLearning demonstrates how to implement a reinforcement learning solution to solve the Cart-Pole gym by programming MyCaffe using Python.

Seq2Seq has several demonstrations that show how to learn a Sin curve using an encoder/decoder model and also how to learn a Sin curve using MNIST hand-written characters that are associated with different parts of the Sin curve.

Loss has several demonstrations that show how to use the various loss functions to solve regresssion, binary classification, multi-class classification and multi-label classification problems.