{"id":167476,"date":"2023-03-30T09:13:29","date_gmt":"2023-03-30T08:13:29","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167476"},"modified":"2023-10-30T18:48:26","modified_gmt":"2023-10-30T17:48:26","slug":"pytorch-all-about-this-framework","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework","title":{"rendered":"PyTorch: all about Facebook&#8217;s Deep Learning framework"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167476\" class=\"elementor elementor-167476\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-23102b3 e-flex e-con-boxed e-con\" data-id=\"23102b3\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-45578c8 elementor-widget elementor-widget-text-editor\" data-id=\"45578c8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>The Python language being one of the most used, it contains a lot of frameworks, and many of them are developed exclusively for Data Science. In this article, we will talk about one of them in detail : PyTorch.<\/b><\/p><p><span style=\"font-weight: 400;\">In the last few years, the popularity of Data Science has been growing steadily over and this has led to an explosion in the resources available to programmers: it is no longer necessary to code by hand. Programming environments such as <\/span><strong><a href=\"https:\/\/pytorch.org\/\">Pytorch<\/a><\/strong><span style=\"font-weight: 400;\">, known as &#8220;frameworks&#8221;, allow complex models to be used in just a few lines.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f3c9910 e-flex e-con-boxed e-con\" data-id=\"f3c9910\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-208840c elementor-widget elementor-widget-heading\" data-id=\"208840c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">What\u2019s the origin of PyTorch ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99c6afe elementor-widget elementor-widget-text-editor\" data-id=\"99c6afe\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Frameworks are made to <\/span><b>make programming easier<\/b><span style=\"font-weight: 400;\">. They are usually developed in &#8220;<\/span><b>open source<\/b><span style=\"font-weight: 400;\">&#8220;, which means that the code is available and editable by all, which allows reliability, transparency and continuous maintenance.<\/span><\/p><p><span style=\"font-weight: 400;\">PyTorch is no exception to this rule. Based on the former Torch library, <\/span><strong><a href=\"https:\/\/pytorch.org\/\">PyTorch<\/a><\/strong><span style=\"font-weight: 400;\"> was officially launched in 2016 by a team from <\/span><b>Facebook&#8217;s research lab<\/b><span style=\"font-weight: 400;\">, and has been developed in open source ever since. The goal of this framework is to <\/span><b>enable the implementation and training of Deep Learning<\/b><span style=\"font-weight: 400;\"> models in a simple and efficient way. Its merger in 2018 with <\/span><strong><a href=\"https:\/\/caffe2.ai\/\">Caffe2<\/a><\/strong><span style=\"font-weight: 400;\"> (another Python framework) has further improved its performance.<\/span><\/p><p><span style=\"font-weight: 400;\">Pytorch is now used by 17% of Python developers (<\/span><a href=\"https:\/\/www.jetbrains.com\/lp\/python-developers-survey-2020\/\"><span style=\"font-weight: 400;\"><strong>Python<\/strong> <strong>Foundation 2020 study<\/strong><\/span><\/a><span style=\"font-weight: 400;\">), and in many companies like Tesla, Uber etc.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fa1202a e-flex e-con-boxed e-con\" data-id=\"fa1202a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-28dcafc elementor-widget elementor-widget-heading\" data-id=\"28dcafc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why use PyTorch?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35edf5c elementor-widget elementor-widget-text-editor\" data-id=\"35edf5c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">PyTorch is a new machine learning library, but it has a lot of <\/span><b>manuals and tutorials<\/b><span style=\"font-weight: 400;\"> where you can find examples. It also has a community that is growing by leaps and bounds.<\/span><\/p><p><span style=\"font-weight: 400;\">PyTorch has a very <\/span><b>simple interface<\/b><span style=\"font-weight: 400;\"> for creating neural networks although it is necessary to work directly with tensors without needing a higher level library like Keras for Theano or Tensorflow.<\/span><\/p><p><span style=\"font-weight: 400;\">Unlike other machine learning tools such as Tensorflow, <\/span><b>PyTorch works with dynamic<\/b><span style=\"font-weight: 400;\"> rather than static graphs. This means that at runtime, features can be changed and the calculation of gradients will vary with them. In contrast, in Tensorflow, one must first define the computational graph and then use the session to compute the tensor results, which <\/span><b>makes debugging the code more difficult<\/b><span style=\"font-weight: 400;\"> and implementation more tedious.<\/span><\/p><p><span style=\"font-weight: 400;\">PyTorch is compatible with graphics cards (GPUs). It uses CUDA internally, an API that connects the CPU to the GPU which has been developed by<\/span><b> NVIDIA<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6c60b16 e-flex e-con-boxed e-con\" data-id=\"6c60b16\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-606b19c elementor-widget elementor-widget-heading\" data-id=\"606b19c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Advantages of PyTorch<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-675e60d elementor-widget elementor-widget-text-editor\" data-id=\"675e60d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">PyTorch has many advantages, here are the main ones:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fc2b8dd elementor-widget elementor-widget-text-editor\" data-id=\"fc2b8dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2><b>\u00a0\u00a01. PyTorch and Python<\/b><\/h2><p><span style=\"font-weight: 400;\">Most of the work related to Machine Learning and Artificial Intelligence is done using Python. However, <\/span><b>PyTorch and Python are related<\/b><span style=\"font-weight: 400;\">, which means that Python developers should feel more comfortable coding with PyTorch than with other Deep Learning frameworks.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61e0e14 elementor-widget elementor-widget-text-editor\" data-id=\"61e0e14\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2><b>\u00a0\u00a0\u00a02. Easy to learn<\/b><\/h2><p><span style=\"font-weight: 400;\">Like the Python language, PyTorch is considered relatively <\/span><b>easier to learn<\/b><span style=\"font-weight: 400;\"> compared to other frameworks. The main reason is due to its simple and intuitive syntax.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1082ea5 elementor-widget elementor-widget-text-editor\" data-id=\"1082ea5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2><b>\u00a03. Strong community<\/b><\/h2><p><span style=\"font-weight: 400;\">Although PyTorch is a relatively new framework, it has developed a dedicated community of developers very quickly. Moreover, PyTorch documentation is very <\/span><b>organized and useful<\/b><span style=\"font-weight: 400;\"> for beginners.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e92d0c elementor-widget elementor-widget-text-editor\" data-id=\"0e92d0c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h2><b>4. Easy debugging<\/b><\/h2><p><span style=\"font-weight: 400;\">PyTorch is deeply integrated with Python, so much so that many Python debugging tools can be easily used with it.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e58be15 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"e58be15\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Become an expert with PyTorch<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e4e4abf e-flex e-con-boxed e-con\" data-id=\"e4e4abf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7780b50 elementor-widget elementor-widget-heading\" data-id=\"7780b50\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Pytorch vs Keras vs Tensorflow : What are the differences ? <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4ed66e elementor-widget elementor-widget-text-editor\" data-id=\"e4ed66e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">It seems difficult to introduce PyTorch without mentioning the other alternatives, all created within a few years of each other with the same goal but different methods.<\/span><\/p><p><b>Keras<\/b><span style=\"font-weight: 400;\"> was developed in March 2015 by Fran\u00e7ois Chollet, a researcher at Google. Keras quickly gained popularity thanks to <\/span><b>its easy-to-use API<\/b><span style=\"font-weight: 400;\">, which is heavily inspired by <\/span><b>scikit-learn<\/b><span style=\"font-weight: 400;\">, the standard <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">Machine Learning<\/a><\/strong><span style=\"font-weight: 400;\"> library in Python.<\/span><\/p><p><span style=\"font-weight: 400;\">A few months later, in November 2015, Google released a <\/span><b>first version of <\/b><a href=\"https:\/\/datascientest.com\/en\/tensor-flow-all-about-googles-machine-learning-framework\"><b>TensorFlow<\/b><\/a><span style=\"font-weight: 400;\">, which quickly became the reference framework for <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/all-about-deep-learning\">Deep Learning<\/a><\/strong><span style=\"font-weight: 400;\">, as it allows you to use Keras. Tensorflow also <\/span><b>developed a number of Deep Learning<\/b><span style=\"font-weight: 400;\"> features that researchers needed to easily create complex neural networks.<\/span><\/p><p><b>Keras was therefore very simple to use<\/b><span style=\"font-weight: 400;\">, but lacked some of the &#8220;low-level&#8221; features or customizations needed for state-of-the-art models. But it didn&#8217;t look like the usual Python style and had very <\/span><b>complicated documentation<\/b><span style=\"font-weight: 400;\"> for beginners<\/span><\/p><p><span style=\"font-weight: 400;\">PyTorch solved these problems by <\/span><b>creating an API <\/b><span style=\"font-weight: 400;\">that is both accessible and easy to customize, allowing the creation of new types of networks, optimizers and architectures. However, recent developments in these frameworks have <\/span><b>brought the way they work much closer together<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d407062 e-flex e-con-boxed e-con\" data-id=\"d407062\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9f43a3f elementor-widget elementor-widget-heading\" data-id=\"9f43a3f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">PyTorch and Deep Learning, does it work together ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2caedac elementor-widget elementor-widget-text-editor\" data-id=\"2caedac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">We have talked about the complexity of models and networks without mentioning the execution speed of algorithms. Indeed, PyTorch is designed to <\/span><b>minimize this time<\/b><span style=\"font-weight: 400;\"> and to <\/span><b>make the best use of the hardware specificities<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">PyTorch represents data as multidimensional arrays, similar to NumPy arrays, called &#8220;tensors&#8221;. The tensors <\/span><b>store the inputs<\/b><span style=\"font-weight: 400;\"> of the neural network, the parameters of the hidden layers and the outputs. From these tensors, PyTorch can perform, in a <\/span><b>hidden and efficient way<\/b><span style=\"font-weight: 400;\">, 4 steps to train the network:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Assemble a graph from the tensors of the neural network, which allows a <\/span><b>dynamic structure<\/b><span style=\"font-weight: 400;\">, it is possible to modify the <\/span><b>neural network<\/b><span style=\"font-weight: 400;\"> (number of nodes, connections between them&#8230;) during training.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Perform the predictions of the network (forward pass)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Compute the loss or error compared to the predictions<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Go through the network in the opposite way : &#8220;<\/span><b>backpropagation<\/b><span style=\"font-weight: 400;\">&#8220;, and adjust the tensors so that the network makes <\/span><b>more accurate predictions<\/b><span style=\"font-weight: 400;\"> based on the calculated loss\/error.<\/span><\/li><\/ul><p>\u00a0<\/p><p><span style=\"font-weight: 400;\">This function of PyTorch called &#8220;Autograd&#8221; is very optimized, and is compatible with the use of <\/span><b>GPUs<\/b><span style=\"font-weight: 400;\"> and <\/span><b>data parallelism<\/b><span style=\"font-weight: 400;\">, which accelerates calculations considerably. Moreover, it allows for use on all clouds, while Tensorflow is only optimized for GoogleCloud and its TPUs (Tensor Processing Unit)..<\/span><\/p><p><span style=\"font-weight: 400;\">Recently, and in partnership with AWS (Amazon Web Services), Pytorch has released 2 new features. The first one, named TorchServe, effectively manages the deployment of already trained neural networks. The second one, <\/span><b>TorchElastic<\/b><span style=\"font-weight: 400;\">, allows the use of Pytorch on <\/span><b>Kubernetes clusters<\/b><span style=\"font-weight: 400;\"> while being resistant to failures.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">These 3 frameworks have their <\/span><b>own specificity<\/b><span style=\"font-weight: 400;\">. In particular PyTorch which is mainly adapted for complex and deep neural networks. To learn to use the framework, we offer <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">Data Scientist training<\/a><\/strong><span style=\"font-weight: 400;\"> with <\/span><b>dedicated modules to Deep Learning<\/b><span style=\"font-weight: 400;\"> with PyTorch.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c7200f elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"2c7200f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Find out more about our Data Science courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-18efad9 e-flex e-con-boxed e-con\" data-id=\"18efad9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cf5e2c5 elementor-widget elementor-widget-html\" data-id=\"cf5e2c5\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"What\u2019s the origin of PyTorch ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"PyTorch is no exception to this rule. 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PyTorch has a very simple interface for creating neural networks although it is necessary to work directly with tensors without needing a higher level library like Keras for Theano or Tensorflow.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Pytorch vs Keras vs Tensorflow : What are the differences ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Keras was developed in March 2015 by Fran\u00e7ois Chollet, a researcher at Google. Keras quickly gained popularity thanks to its easy-to-use API, which is heavily inspired by scikit-learn, the standard Machine Learning library in Python. A few months later, in November 2015, Google released a first version of TensorFlow, which quickly became the reference framework for Deep Learning, as it allows you to use Keras. 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The tensors store the inputs of the neural network, the parameters of the hidden layers and the outputs.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The Python language being one of the most used, it contains a lot of frameworks, and many of them are developed exclusively for Data Science. In this article, we will talk about one of them in detail : PyTorch. In the last few years, the popularity of Data Science has been growing steadily over and [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":167477,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>PyTorch: all about Facebook&#039;s Deep Learning framework<\/title>\n<meta name=\"description\" content=\"The Python language being one of the most used, it contains a lot of frameworks, and many of them are developed exclusively for Data Science.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/pytorch-all-about-this-framework\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PyTorch: all about Facebook&#039;s Deep Learning framework\" \/>\n<meta property=\"og:description\" content=\"The Python language being one of the most used, it contains a lot of frameworks, and many of them are developed exclusively for Data Science. 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