Today I presented everything I did over the summer. I felt that all of the presentations went great and I am very grateful to RIT as well as my mentors for this amazing experience. Here is my presentation.
At the start of today I watched 2 of Stanford's lectures one on feed-forward neural networks and the other on convolutional neural networks. In a feed-forward network there is no connection to previous nodes. This is a great visualization of the relationship between nodes and how a previous layer provides input for a future layer. The other type of neural network is a recurrent neural network. What makes a recurrent network different is that outputs on a certain layer can act as inputs for the previous layer. I have not gone into depth on recurrent networks so my knowledge is limited. A convolutional network is not another type of network but rather a specific layer on a neural network which alters the data. One example of convolution is pooling. As demonstrated below pooling takes a filter full of values and then averages or sums them creating a new array. Pooling is useful because it reduces the size of your matrix while retaining a similar value. This allows for calculations to...
Most of today was spent working on my program. Despite some initial struggles with importing data and feeding that data to the network I was able to extract a feature vector by the end of the day. A feature vector is a list of values given by the pre-trained neural network that can be used to classify images. Using this feature vector I was able to classify 10 images with 100% accuracy. Although with a larger data set that accuracy may have fallen I am still extremely impressed with the application of a pre-trained neural network. Source: https://brilliant.org/wiki/feature-vector/
Today I began to experiment with using a pre-trained neural network in order to have a higher accuracy in testing. I used resnet-18 which is a neural net that has been trained on a data set with 14 million images. This allows resnet-18 to identify specific features like edges or corners. The detection of these edges is called feature identification and it is a large advantage of using a pre-trained neural network. Using resnet-18 also reduces training time because the network already has a good idea on how to tell apart images. I plan to test my pre-trained neural net on the caltech-101 data set later this week. Source: http://web.eecs.umich.edu/~honglak/cacm2011-researchHighlights-convDBN.pdf
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