{"id":9143,"date":"2021-09-06T11:22:58","date_gmt":"2021-09-06T09:22:58","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=9143"},"modified":"2023-09-12T10:49:07","modified_gmt":"2023-09-12T08:49:07","slug":"pytorch-vs-tensorflow","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/pytorch-vs-tensorflow\/","title":{"rendered":"PyTorch vs TensorFlow: What Are They, and Which Should You Use?"},"content":{"rendered":"

Most people choose to begin their adventures with machine learning<\/a> by using either PyTorch or TensorFlow. But how do you choose?
\n<\/strong>
\n<\/span>PyTorch and TensorFlow are leading machine learning libraries<\/a> used to power thousands of highly intelligent applications. Developed by the big players in tech\u2014Meta’s Artificial Intelligence Research lab and Google\u2019s Brain team, these platforms deal with deep learning. <\/span>
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\n<\/span>In this article, I’ll talk you through PyTorch vs TensorFlow and explain the use case for each of them. You can also use the following clickable menu to skip forward to a section you\u2019re interested in.<\/span><\/p>\n

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  1. What is deep learning?<\/a><\/strong><\/li>\n
  2. What is PyTorch?<\/a><\/strong><\/li>\n
  3. What is TensorFlow?<\/a><\/strong><\/li>\n
  4. PyTorch vs TensorFlow: Which should you use?<\/a><\/strong><\/li>\n
  5. Key takeaways and next steps<\/a><\/strong><\/li>\n<\/ol>\n

    With that, let\u2019s get started!<\/span><\/p>\n

    1. What is deep learning?<\/span><\/h2>\n

    If you\u2019ve heard about PyTorch and TensorFlow, you may have also heard about deep learning, but what exactly is it? Let\u2019s recap to find out. <\/span>
    \n<\/span>
    \n<\/span>Highly intelligent computer programs capable of \u2018learning\u2019 have been around for a couple of decades now. The modern ones use an ingenious technique called deep learning. <\/span>    Deep learning employs an artificial neural network with three or more layers. <\/span><\/a>These networks attempt to learn from large sets of data\u2014and when we say large, we mean large<\/strong>. Now, while a single layer of neural networks can make approximate predictions on its own, it\u2019s the multi-layer structure that makes deep learning algorithms so incredibly accurate.\u00a0<\/span><\/p>\n

    Deep learning drives many artificial intelligence (AI<\/a>) applications and services that drive automation and perform analytical and physical tasks without human intervention. These applications are everywhere around you\u2014from something as seemingly simple as a weather app on your phone, to more sophisticated state-of-the-art gadgets like self-driving cars. <\/span><\/p>\n

    Google\u2019s image-based CAPTCHA (yes, that annoying process of selecting all the boxes with cars in them to prove you\u2019re not a bot before sending a form), these applications are used to feed their deep learning programs. These programs then process billions of bytes of data and create algorithms that power things like Google\u2019s self-driving cars!<\/span><\/p>\n

    \u200b\u200bBut how do you start dipping your feet in the data analytics pond? In this post, we\u2019ll compare two of the most popular deep learning programs: PyTorch and TensorFlow.\u00a0<\/span><\/p>\n

    Learn more:<\/strong> What\u2019s the Difference Between Machine Learning and Deep Learning?<\/a><\/p>\n

    2. What is PyTorch?\u00a0<\/span><\/h2>\n

    Simply put, PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab (now renamed Meta AI<\/a>). <\/span><\/p>\n

    It was first introduced in 2016 and has since been distributed on the BSD license as free software. The name, interestingly enough, is a combination of two words you are probably familiar with: Python and Torch. <\/span>
    \n<\/span>
    \n<\/span>Python is the software\u2019s user interface, while Torch is one of the first machine learning libraries released way back in 2002. The use of the name Torch here is more than just a subtle homage: PyTorch shares some of its C++ backend with Torch, thus allowing users to program on it using C\/C++.\u00a0<\/span><\/p>\n

    Learn more:<\/strong> What\u2019s the Best Language for Machine Learning?<\/a><\/p>\n

    Advantages of using PyTorch<\/span><\/h3>\n