{"id":67611,"date":"2026-01-28T12:55:53","date_gmt":"2026-01-28T11:55:53","guid":{"rendered":"https:\/\/multi.liora.io\/?p=67611"},"modified":"2026-08-08T12:24:15","modified_gmt":"2026-08-08T11:24:15","slug":"tensor-flow-all-about-googles-machine-learning-framework","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/tensor-flow-all-about-googles-machine-learning-framework","title":{"rendered":"Tensor flow : All about Google\u2019s Machine Learning framework"},"content":{"rendered":"\n

TensorFlow is a Machine Learning framework created by Google and available in open source. Discover everything you need to know about it: history, functioning, advantages, training\u2026<\/strong><\/p>\n\n\n

Machine Learning is a complex discipline, but fortunately there are tools that simplify its implementation. Among these resources, there is the TensorFlow framework<\/strong> from Google.<\/p>\n\n\n

What is TensorFlow?<\/h2>\n\n\n

It is an open-source library for numerical computation and Machine Learning compatible with the Python language<\/a><\/strong>. It simplifies the process of acquiring data<\/strong>, training Machine Learning models, generating predictions and refining future results.<\/p>\n\n\n

TensorFlow combines Machine Learning<\/a><\/strong> and Deep Learning<\/strong><\/a> models and algorithms. The provides a convenient and comfortable front-end API<\/strong> for creating applications using this framework. These applications can then be executed in C++<\/strong>.<\/p>\n\n\n

The framework can be used to train and run deep neural networks<\/strong> for handwritten digit classification, image recognition, lexical embedding, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and simulations based on partial differential equations.<\/p>\n\n\n

The history of TensorFlow<\/h2>\n\n\n

TensorFlow was initially created by the Google Brain team<\/strong>. The goal was originally to use neural networks to improve Google services such as Gmail, Photos or the search engine.<\/p>\n\n\n

Thanks to this framework, researchers and developers could work together on an AI model<\/strong>. Subsequently, TensorFlow was made public at the end of 2015 for the first time. However, the first stable release dates back to 2017.<\/p>\n\n\n

This tool is open source under the Apache Open Source license<\/strong>. Therefore, it is possible to use it, modify it and redistribute the modified version in commercial form without paying Google.<\/p>\n\n\n

How does TensorFlow work?<\/h2>\n\n\n

Developers can create \u201cdataflow graphs<\/strong>\u201d. These are structures to describe how data is transferred through graphs or a series of processing nodes. Each node in the graph represents a mathematical operation. Each connection between the nodes is a multidimensional data array: a tensor<\/strong>.<\/p>\n\n\n

Interaction with TensorFlow is done through the Python language<\/a><\/strong>, which is both simple to learn and to use<\/strong>. This language makes it easy to express how high-level abstractions can be coupled together.<\/p>\n\n\n

TensorFlow nodes and tensors are Python objects. TensorFlow applications, themselves, are Python applications.<\/p>\n\n\n

However, the mathematical operations themselves are not performed in Python. The transformation libraries accessible via TensorFlow are written as high-performance C++ binaries<\/strong>. The Python language simply directs traffic between the various parts and allows them to be connected through high-level abstractions.<\/p>\n\n\n

TensorFlow applications can run on a local machine, a cluster in the cloud<\/strong>, iOS or Android smartphones and tablets, or CPUs and GPUs. On its own Cloud, Google offers to run TensorFlow<\/strong> on its TensorFlow Processing Unit <\/strong>(TPU) chips to take advantage of increased acceleration<\/strong>. The models created by TensorFlow can then be deployed on any device to deliver predictions.<\/p>\n\n\n

With TensorFlow 2.0, launching in October 2019, the framework has undergone a major overhaul. The changes made are based on user feedback This new version is more powerful and easier to use<\/strong>, especially via the use of the Keras API<\/strong> for model training. A new API simplifies distributed training.<\/p>\n\n\n

Compatibility with TensorFlow Lite<\/strong> allows models to be deployed on a wider variety of platforms. The only downside is that code written for earlier versions of TensorFlow must be rewritten to take full advantage of the new features in TensorFlow 2.0.<\/p>\n\n\n

Learn to use TensorFlow<\/a><\/div><\/div>\n\n\n