{"id":67611,"date":"2026-01-28T12:55:53","date_gmt":"2026-01-28T11:55:53","guid":{"rendered":"https:\/\/multi.liora.io\/?p=67611"},"modified":"2026-02-06T07:25:49","modified_gmt":"2026-02-06T06:25:49","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":"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<\/b>\n\nMachine Learning is a complex discipline, but fortunately there are tools that simplify its implementation. Among these resources, there is the TensorFlow framework<\/b> from Google.\n

What is TensorFlow?<\/h2>\nIt 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<\/b>, training Machine Learning models, generating predictions and refining future results.\n\nTensorFlow combines Machine Learning<\/a><\/strong> and Deep Learning<\/strong><\/a> models and algorithms. The provides a convenient and comfortable front-end API<\/b> for creating applications using this framework. These applications can then be executed in C++<\/b>.\n\nThe framework can be used to train and run deep neural networks<\/b> 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.\n

The history of TensorFlow<\/h2>\nTensorFlow was initially created by the Google Brain team<\/b>. The goal was originally to use neural networks to improve Google services such as Gmail, Photos or the search engine.\n\nThanks to this framework, researchers and developers could work together on an AI model<\/b>. Subsequently, TensorFlow was made public at the end of 2015 for the first time. However, the first stable release dates back to 2017.\n\nThis tool is open source under the Apache Open Source license<\/b>. Therefore, it is possible to use it, modify it and redistribute the modified version in commercial form without paying Google.\n

How does TensorFlow work?<\/h2>\nDevelopers can create \u201cdataflow graphs<\/b>\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<\/b>.\n\nInteraction with TensorFlow is done through the Python language<\/a><\/strong>, which is both simple to learn and to use<\/b>. This language makes it easy to express how high-level abstractions can be coupled together.\n\nTensorFlow nodes and tensors are Python objects. TensorFlow applications, themselves, are Python applications. \n\nHowever, the mathematical operations themselves are not performed in Python. The transformation libraries accessible via TensorFlow are written as high-performance C++ binaries<\/b>. The Python language simply directs traffic between the various parts and allows them to be connected through high-level abstractions.\n\nTensorFlow applications can run on a local machine, a cluster in the cloud<\/b>, iOS or Android smartphones and tablets, or CPUs and GPUs. On its own Cloud, Google offers to run TensorFlow<\/b> on its TensorFlow Processing Unit <\/b>(TPU) chips to take advantage of increased acceleration<\/b>. The models created by TensorFlow can then be deployed on any device to deliver predictions.\n\nWith 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<\/b>, especially via the use of the Keras API<\/b> for model training. A new API simplifies distributed training.\n\nCompatibility with TensorFlow Lite<\/b> 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.\n\n\n
Learn to use TensorFlow<\/a><\/div><\/div>\n\n