Skip to main content
Crowdfunding
Python + AI for Geeks
Practice

TensorFlow - Library for Machine Learning and Deep Learning

TensorFlow is an open-source machine learning and deep learning library developed by the Google Brain team.

It is primarily used to build and train deep learning models and is designed to handle large-scale data processing and parallel computing.

Parallel computation refers to the technique of performing multiple computations simultaneously to enhance calculation speed.

TensorFlow supports execution on various platforms and can operate in different hardware environments such as CPU, GPU (Graphics Processing Unit), and TPU (Tensor Processing Unit).

Additionally, it provides interfaces for various programming languages such as Python, C++, and JavaScript, allowing for the development of machine learning and deep learning models in a wide range of environments.

Since Google released it as open-source in November 2015, it has become an essential tool for companies and research institutions to develop AI models.


Why TensorFlow is Widely Used​

TensorFlow, as an open-source project, is available to everyone, forming a vibrant community where developers around the world contribute.

AI models can also be executed on mobile and IoT devices, and simple APIs (interfaces) like Keras can be utilized to quickly build AI models.


Key Features of TensorFlow​

TensorFlow offers the following features:

  • Deep Learning Model Construction: Design and implement complex neural network models more easily.

  • Automatic Differentiation: Automatically performs the differentiation operations needed for weight updates during the learning process.

  • GPU and TPU Acceleration: Improve model training speed by utilizing GPU and TPU.

  • Model Saving and Loading: Save trained models and reload them when needed for reuse.

  • TensorBoard Support: Visually analyze and debug the training process.

  • Web and Mobile Support: Execute AI models in a web browser using TensorFlow.js or deploy them on mobile devices using TensorFlow Lite.


In TensorFlow, a tensor is the basic unit used to represent data.

A tensor is an N-dimensional array, used in machine learning and deep learning to process various types of data.

An N-dimensional array is a structure that uses multiple indices to store and access data. Simply put, it is a nested structure where arrays (or lists in Python) can contain other arrays.

For example, a single number can be represented as a 0-dimensional tensor (scalar), a list of numbers as a 1-dimensional tensor (vector), and a matrix as a 2-dimensional tensor.

We'll explore tensors in more detail in the next lesson.

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.