Python Deep Learning: PyTorch vs Tensorflow (Overview)

Python Deep Learning: PyTorch vs Tensorflow Negar Vahid 02:01

PyTorch vs TensorFlow: What’s the difference? Both are open source Python libraries that use graphs to perform numerical computation on data. Both are used extensively in academic research and commercial code. Both are extended by a variety of APIs, cloud computing platforms, and model repositories.

If they’re so similar, then which one is best for your project?

In this video course, you’ll learn:

What the differences are between PyTorch and TensorFlow
What tools and resources are available for each
How to choose the best option for your specific use case

You’ll start by taking a close look at both platforms, beginning with the slightly older TensorFlow, before exploring some considerations that can help you determine which choice is best for your project. Let’s get started!

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Course Slides (.pdf)

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Sample Code (.zip)

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00:00 Welcome to the PyTorch versus TensorFlow course. I’m Nagar with Real Python, and I’ll be your guide.

00:07 When you’re starting to work on a machine learning project, one of the first choices you have to make is whether to create your model using TensorFlow or PyTorch.

00:16 This course will help you decide which one of the two works better for your project, how In the next few lessons, you’ll grasp the fundamentals of tensors, which are the backbone of both of the frameworks.

00:29 You’ll explore the general features of TensorFlow and PyTorch. You’ll become familiar with their coding style and explore a few code snippets. You’ll delve into Torch and Keras frameworks.

00:42 You’ll see what Torch is and how Keras makes TensorFlow a lot more convenient to use.

00:48 And finally, you’ll be acquainted with the ecosystems of TensorFlow and PyTorch. For example, you’ll get to know tools and websites that offer pre-trained models and built-in datasets and so on.