Transfer Learning Last time we talked about Inception Network and some other Neural Network architectures. All of these Neural Network architectures are really big and hard to train and we have a problem. So if we are building a computer vision application rather than training a neural network from scratch we often make much faster progress if we download the network’s weights. In other words someone else has already trained the network architecture and we…
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Inception Network In the previous post we’ve already seen all the basic building blocks of the Inception network. Here, we will see how to put these building blocks together and build it. An example of an Inception module To explain how Inception Network works we will consider a few steps: The third step may be (look at a red rectangle) just using a \(1\times 1 \) convolution, maybe with \(64\) filters so we get a $latex  28\times…
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Network in Network – 1×1 Convolutions In terms of designing \(ConvNet \) architectures one of the ideas that really helps is using a \(1\times 1 \) convolution. You might be wondering what does \(1\times 1 \) convolution do? Isn’t that just multiplying by a number? It seems like a funny thing to do. However, it turns out that it’s not quite like that. Let’s take a look! What does a \(1\times 1 \) convolution do?…
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Why \(ResNets \) work? Why do \(ResNets \) work so well? Let’s go through one example that illustrates why \(ResNets \) work so well, at least in the sense of how we can make them deeper and deeper without really hurting our ability to get them to do well on the training set. Hopefully, doing well on the training set is usually a prerequisite to doing well on the test set. So, being able to…
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Residual networks Last time we saw how VGG Networks work, however very deep neural networks are difficult to train because of the vanishing and exploding gradients types of problems. In this post we’ll learn about skip connections which allows us to take the activation from one layer and feed it to another layer much deeper in the neural network.  Using that we will build \(Resnets \) which enable us to train very deep networks, that…
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