Preliminaries

2. Preliminaries

2.1. Data Manipulation

2.2. Data Preprocessing

2.3. Linear Algebra

2.4. Calculus

2.5. Automatic Differentiation

2.6. Probability

2.7. Documentation

Tensor

No matter which framework you use, itstensor class**(ndarrayin MXNet, Tensorin both PyTorch and TensorFlow)** is similar to NumPy'sndarraywith a few killer features. First, GPU is well-supported to accelerate the computation whereas NumPy only supports CPU computation. Second, the tensor class supports automatic differentiation. These properties make the tensor class suitable for deep learning.

A tensor represents a (possibly multi-dimensional) array of numerical values. With one axis, a tensor corresponds (in math) to a vector. With two axes, a tensor corresponds to a matrix. Tensors with more than two axes do not have special mathematical names.

It provides a variety of functionalities including basic mathematics operations, broadcasting, indexing, slicing, memory saving, and conversion to other Python objects