TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow is designed to be flexible, scalable, and efficient, making it suitable for a wide range of applications, from research to production systems.

Here's how TensorFlow works:

Computation Graph:

TensorFlow represents computations as a directed graph called the computation graph. Nodes in the graph represent operations, while edges represent the flow of data (tensors) between operations. The graph defines the structure of the computation and allows TensorFlow to optimize and parallelize the execution of operations.

Tensors:

Tensors are multi-dimensional arrays that represent data in TensorFlow. They serve as the fundamental data structure for storing input data, model parameters, and intermediate results during computation. Tensors can have various shapes and data types, such as scalars, vectors, matrices, or higher-dimensional arrays.

Operations:

TensorFlow provides a wide range of operations (ops) for performing mathematical computations, data manipulation, and machine learning tasks. These operations include arithmetic operations, matrix multiplication, activation functions, loss functions, optimization algorithms, and more.