All transformations learned by deep neural networks can be reduced to a handful of tensor operations that are applied to tensors of numeric data. In the First example, the network was built by stacking dense layers on top of each other. A layer instance looked like: layer_dense(units = 512, activation = "relu") This layer can… Continue reading Tensor operations
Month: May 2022
Data representations
In the First example, we started from data stored in multidimensional arrays, which are also called tensors. Tensors are a generalisation of vectors and matrices to an arbitrary number of dimensions (in the context of tensors, a dimension is often called an axis). In R, vectors are used to create and manipulate 1D tensors and… Continue reading Data representations
First example
Understanding deep learning requires familiarity with: Tensors Tensor operations Differentiation Gradient descent As a first example, we will try to classify grayscale images of handwritten digits (28 by 28 pixels) into their 10 categories (0-9). This example uses the MNIST dataset containing 60,000 training images and 10,000 test images, assembled by the National Institute of… Continue reading First example