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Phd thesis convolutional neural network

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With David Rumelhart and Ronald J. Williams , Hinton was co-author of a highly cited paper published in that popularized the backpropagation algorithm for training multi-layer neural networks, [14] although they were not the first to propose the approach. Hinton was educated at King's College, Cambridge , graduating in with a Bachelor of Arts in experimental psychology. After his Ph. Hinton taught a free online course on Neural Networks on the education platform Coursera in He is planning to "divide his time between his university research and his work at Google".
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Jingwei Huang

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sercmig - Ravi Prasad K. J. Thesis Work

Malek, Salim Deep neural network models for image classification and regression. PhD thesis, University of Trento. Deep learning, a branch of machine learning, has been gaining ground in many research fields as well as practical applications. Such ongoing boom can be traced back mainly to the availability and the affordability of potential processing facilities, which were not widely accessible than just a decade ago for instance. Although it has demonstrated cutting-edge performance widely in computer vision, and particularly in object recognition and detection, deep learning is yet to find its way into other research areas. This, thereby, raises not only precision concerns but also processing overheads.
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Deep Learning Based Sentiment Analysis Using Convolution Neural Network

Dramsch, J. Complex-valued neural networks for machine learning on non-stationary physical data. This work implements self-supervised aes that compress the data and measure the reconstruction of the seismic data. Four different deep convolutional aes are constructed. Two aes are real-valued and two aes are complex-valued.
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Andrej Karpathy. It's been a while since I graduated from Stanford. My main webpage has moved to karpathy.
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