Abstract
Deep neural networks are becoming the state-of-the-art in the field of machine learning, especially because of their notable performance in a variety of current problems. Among these algorithms we can cite convolutional neural networks as one of the most powerful models for image classification problems. This research aimed to perform a theoretical study on deep neural networks, as well as to implement a convolutional neural network applied to the problem of handwritten digits classification.
References
Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning. 2016.
LeCun, Y. The MNIST Database of Handwritten Digits. Available at: http://yann.lecun.com/ exdb/mnist/ . Last acessed in: 07/07/2019.
Gonçalves, R. MNIST nn. Avaiable at: https://github.com/RafaelGoncalves8/ mnist_nn/. Last acessed in: 07/07/2019.
LeCun, Y. The MNIST Database of Handwritten Digits. Available at: http://yann.lecun.com/ exdb/mnist/ . Last acessed in: 07/07/2019.
Gonçalves, R. MNIST nn. Avaiable at: https://github.com/RafaelGoncalves8/ mnist_nn/. Last acessed in: 07/07/2019.
Todos os trabalhos são de acesso livre, sendo que a detenção dos direitos concedidos aos trabalhos são de propriedade da Revista dos Trabalhos de Iniciação Científica da UNICAMP.
Downloads
Download data is not yet available.