Category Archives: Keras

The convolution layer uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. The resulting output is called a feature map or activation map. 

That max pooling isn’t the only way you can achieve downsampling. You can also use strides in the prior convolution layer or you can use average pooling instead of max pooling,

Average pooling was a common approach early on but has fallen out of favor somewhat. As you might expect, it works exactly like a max pooling layer, except it computes the mean rather than the max.

For a multiclass classification problem, you can use the one-versus-all design and find a ROC curve for each class. The one-versus-all treats a multiclass classification problem as a set of binary classification problems

It’s a softmax activation plus a Cross-Entropy loss used for multiclass classification. Using this loss, we can train a Convolutional Neural Network to output a probability over the N classes for each image.

In this tutorial, We will Install the Transformers library, set up your cache, and download the pre-train Transformers model to run offline and load the model from a local directory.

Dropout and Batch Normalization often lead to a worse performance when they are combined together in many modern neural networks, but cooperate well sometimes as in Wide ResNet (WRN).

PyTorch, Scikit, and Keras, have some form of built-in MNIST dataset designed to work with the library. But there are problems with using a built-in dataset. data access becomes a magic black box.

TensorFlow users on Mac powered by Apple’s new M1/M2 chip can now take advantage of accelerated training using Apple’s Mac-optimized version of TensorFlow 2.4.

The transformers API makes it possible to save all of these pieces to disk at once, saving everything into a single archive in the PyTorch or TensorFlow saved model format.