3.6 Training a Logistic Regression Model in PyTorch – Parts 1-3
What we covered in this video lecture
In this lecture, we put all the basic concepts into action:
- We implemented a logistic regression model using the
- We then trained the logistic regression module by implementing a training loop based on PyTorch’s automatic differentiation capabilities.
After completing this lecture, we now have all the essential tools for implementing deep neural networks in the next unit: activation functions, loss functions, and essential deep learning utilities of the PyTorch API.
But before we jump into the next unit, we will cover a small but critical concept that is essential for training neural networks well: input feature normalization.
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