Video course · 8 chapters · 34 min · certificate
Python Deep Learning Foundations: TensorFlow, Keras & PyTorch Essentials
Deep learning foundations in Python: neural networks as universal function approximators, deep learning vs classic ML, TensorFlow and Keras, a TensorFlow implementation, PyTorch and a PyTorch implementation.
What you'll learn
- Explain what a neural network is
- Contrast deep learning with classic ML
- Relate Keras to TensorFlow
- Train a model in TensorFlow/Keras
- Define and train a model in PyTorch
- Choose between frameworks
Chapters
8 chapters · 34:17-
1:11
01Intro Members
Introduction
Three deep learning frameworks.
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3:44
02Concepts Members
Neural network basics
Universal function approximators.
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3:41
03Concepts Members
Deep learning vs ML
When deep learning fits.
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3:55
04Frameworks Members
TensorFlow and Keras
How they relate.
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9:20
05Code Members
TensorFlow implementation
Data, model, training.
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5:33
06Frameworks Members
PyTorch overview
PyTorch's approach.
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5:22
07Code Members
PyTorch implementation
nn.Module and a training loop.
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1:31
08Wrap-up Members
Conclusion
Recap of the three frameworks.
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