Written by Vihar Kurama. Author takes a look at two popular frameworks and compares them: PyTorch vs. TensorFlow. In simple terms deep learning is an add-on to develop human-like computers to solve real-world problems with its special brain-like architectures called artificial neural networks.
This article dives into:
- Google’s TensorFlow
- Facebook’s PyTorch
- What can we build with TensorFlow and PyTorch?
- Comparing PyTorch and TensorFlow
- Pros and cons of PyTorch and TensorFlow
- PyTorch and TF installation, versions, updates
- TensorFlow vs. PyTorch: recommendation
One main feature that distinguishes PyTorch from TensorFlow is data parallelism. PyTorch optimizes performance by taking advantage of native support for asynchronous execution from Python.
You will also get links to top projects for each framework and links to useful resources. Nice one!
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