The deep learning libraries we will use in this book are MXNet, Keras, and TensorFlow. Keras is a frontend API, which means it is not a standalone library as it requires a lower-level library in the backend, usually TensorFlow. The advantage of using Keras rather than TensorFlow is that it has a simpler interface. We will use Keras in later chapters in this book.
Both MXNet and TensorFlow are multipurpose numerical computation libraries that can use GPUs for mass parallel matrix operations. As such, multi-dimensional matrices are central to both libraries. In R, we are familiar with the vector, which is a one-dimensional array of values of the same type. The R data frame is a two-dimensional array of values, where each column can have different types. The R matrix is a two-dimensional array of values with the same type. Some machine...
