Titlebook: Convolutional Neural Networks with Swift for Tensorflow; Image Recognition an Brett Koonce Book 2021 Brett Koonce 2021 convolutional neural

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Convolutional Neural Networks with Swift for TensorflowImage Recognition an

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Convolutional Neural Networks with Swift for Tensorflow978-1-4842-6168-2

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ResNet 34,apters, the difference between our 2D MNIST, CIFAR, and VGG networks is simply the number of blocks of 3x3 convolutions. Why stop at this point, though? Let‘s make even larger networks! Next, we‘re going to look at the ResNet family of networks, starting with ResNet 34.

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ResNet 50,r results to a ResNet 50 baseline, and it is valuable as a reference point. As well, we can easily download the weights for ResNet 50 networks that have been trained on the Imagenet dataset and modify the last layers (called **retraining** or **transfer learning**) to quickly produce models to tackl

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EfficientNet, going to look at different variants of the same basic idea of having the computer explore different neural network architectures for us. We will look at some of the research which builds up to our next neural network, EfficientNet, which was partially built using these techniques.

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