TensorFlow Numerical Operations
TensorFlow, empowers users with a robust set of numerical operations, forming the backbone of its computational capabilities. These operations, executed on tensors, the fundamental data structures in TensorFlow, facilitate complex mathematical computations essential for machine learning tasks.
TensorFlow excels in handling a diverse range of numerical operations, including but not limited to matrix multiplications, element-wise operations, and various mathematical transformations.
Tensors
TensorFlow represents data using tensors, represented as tf.Tensor objects. which are multidimensional arrays. Tensors can hold numbers (scalars), vectors (single-dimensional arrays), matrices (two-dimensional arrays), or even higher-dimensional data structures.
Numerical Operations in TensorFlow
TensorFlow is an open-source machine-learning library developed by Google. TensorFlow is used to build and train deep learning models as it facilitates the creation of computational graphs and efficient execution on various hardware platforms. Here, we will learn some of the basic Numerical operations available in TensorFlow and how they can be used.
Table of Content
- TensorFlow Numerical Operations
- Mathematical Operations with Tensors
- Element-wise Operations with Tensors
- Aggregations and Statistics Operations with Tensors
- Automatic Differentiation with Tensors