April 2017
Intermediate to advanced
320 pages
7h 46m
English
Content preview from Deep Learning with TensorFlow
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Summary
We have seen how to implement a feed-forward neural network (ffnn) architecture for an image classification problem.
An ffnn is characterized by a set of input units, a set of output units, and one or more hidden units that connect the input level from that output. The connections between the levels are total and in a single direction: each unit receives a signal from all the units of the previous layer and transmits its output value, suitably weighed to all units of the next layer. For each layer a transfer function (sigmoid, softmax, ReLU) must be defined: the choice of the transfer function depends on the architecture and then the addressed problem.
Then we implemented four different ffnn models, the first model with a single hidden ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.