Machine learning software library.
Tensorflow is an open-source software library developed by the Google Brain team. It was designed to conduct machine learning and deep neural network research. However, it is general enough to be applicable in a wide variety of other domains as well.
At its core, Tensorflow is a system for executing computational graphs over Tensor objects, with native support for performing backpropagation for its Variables. In it, we describe computations as graphs. Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors (multi-dimensional arrays), performs some computation, and produces zero or more Tensors.
Tensorflow is widely used in the field of deep learning and artificial intelligence. It provides a platform for building and designing models, serving inferences, and training these models. Tensorflow can be used for various applications such as voice/sound recognition, text-based applications, image recognition, time-series data, and video detection.
Tensorflow architecture works in three parts:
It is called Tensorflow because it takes input as a multi-dimensional array, also known as tensors. You can construct a sort of flowchart of operations (called a Graph) that you want to perform on that input. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output. This is why it is called Tensorflow!
There are two versions of Tensorflow available: Tensorflow 1 and Tensorflow 2. Tensorflow 2 is an updated version of Tensorflow 1 with more features and ease of use.
To install Tensorflow, you need to have Python installed on your system. Once Python is installed, you can use the following command to install Tensorflow:
pip install tensorflow
If you want to install a specific version of Tensorflow, you can do so by specifying the version number:
pip install tensorflow==2.0.0
In conclusion, Tensorflow is a powerful library for numerical computation, particularly well suited and fine-tuned for large-scale Machine Learning. Its basic principle is simple, yet it can be used to build very complex computations.
My dude, any questions for me?