Difference between Numpy and TensorFlow

What is the difference between Numpy and TensorFlow?

Numpy and TensorFlow are both popular libraries in the field of machine learning and data science.

Numpy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

On the other hand, TensorFlow is an open-source machine learning framework developed by Google. It is designed for building and training machine learning models, particularly deep learning models.

TensorFlow provides a flexible architecture for numerical computation across a range of platforms and is often used for tasks such as image recognition, natural language processing, and more. In summary, Numpy is primarily focused on numerical computing and array manipulation, while TensorFlow is a comprehensive machine learning framework with a focus on deep learning and neural networks.

Both Numpy and TensorFlow are N-d Array Libraries. Tensors are multilinear maps from Vector spaces to real numbers. Scalar, Vector and Matrix are all tensors. So a tensor could be represented as a multi-dimensional array.

Numpy has N-d array support but does not have methods to create tensor functions, can’t automatically compute derivatives and it can’t take advantage of GPU.

TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives. TensorFlow computations define a graph that has no numerical value until evaluated. This graph can be seen as having each operation as node and Tensors being transformed at each node and propagating to the next operation in the graph