Deep Learning in Python: How to Choose a Framework?
summary
Compares current deep-learning frameworks (TensorFlow, Keras, PyTorch, Fastai, JAX) and explains concrete differences between them. Presents practical criteria—GPU support, NLP workflow, and model deployment needs—derived from R&D project experience to help choose the most appropriate framework for a given project.
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chapters (18)
- 0:00Introduction
- 1:29What is deep learning
- 6:21Key coding concepts
- 7:03PyTorch overview
- 8:28TensorFlow overview
- 9:51JAX overview
- 11:13MXNet overview
- 12:16PyTorch demo
- 16:08TensorFlow demo
- 17:29Eager mode in TensorFlow
- 19:15Data preprocessing
- 20:19General libraries
- 23:31NLP libraries
- 25:28Vision libraries
- 26:53Optimization libraries
- 27:39Choosing a framework
- 34:19In-demand frameworks
- 35:00Conclusion
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