In this chapter, we started by giving the definition of an artificial neural network, and showed you how individual ANs can be combined into dense layers, which combine together into a full-on deep neural network. We then implemented a dense layer in CUDA-C and made an appropriate corresponding Python wrapper class. We also included functionality to add ReLU and sigmoid layers on the outputs of a dense layer. We saw the definition and motivation of using a softmax layer, which is used for classification problems, and then implemented this in CUDA-C and Python. Finally, we implemented a Python class so that we could build a sequential feed-forward DNN from the prior classes; we implemented a cross-entropy loss function, and then used this in our loss function in our implementation of gradient descent to train the weights and biases in our DNN. Finally, we used our implementation...
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You're reading from Hands-On GPU Programming with Python and CUDA
Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Read more about Dr. Brian Tuomanen
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Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Read more about Dr. Brian Tuomanen