HTC Education Series: Deep Learning with PyTorch Basics - Lesson 5
After a short holiday break (and software release crunch time, oh joy), let's continue with lesson 5 of our HTC Deep Learning with PyTorch Basic course. As usual for this course, we'll be starting off with the 5th lecture in the Jovian.ai course called 'Deep Learning with PyTorch: Zero to GANs'. And what a lecture it is. The best so far in this series (so we look forward with anticipation to lesson 6 and GANs).
In this jam packed to the gills lesson, you will learn how to code a state of the art deep learning model from scratch in PyTorch (how far we have come in 5 short lessons). We'll also learn very useful techniques like data augmentation, regularization, and adding residual layers.
Plus we'll be using the Adam optimizer rather than stochastic gradient descent. Adam uses techniques like momentum and adaptive learning rates for faster training.
And we'll learn about weight decay, gradient clipping, and learning rate scheduling.
Let's get started.
What was covered in the lecture
learning rate scheduling
how to train your state of the art model
Don't forget to check out the associated Jupyter notebooks
ResNets, regularization and data augmentation - here
SimpleCNN Starter Notebook - here
Transfer Learning with CNNs - here
Image classification with CNNs - here
Additional HTC Material
1. Now that you are familiar with some activation and loss functions used in deep learning, you're ready for Yann LeCunn's deep dive lecture on PyTorch Activation and Loss Functions. Collect them all for your PyTorch programming toolbox.
1. The jovian lectures started off a little bit too basic for my tastes (but perhaps perfect for yours if you are just getting started with all of this. But they really pulled things together for this lesson 5 lecture. It provides a very clear presentation of a wide variety of advanced topics, presented in a way that is easy to comprehend (not always the case in a course like this).
2. Your PyTorch toolbox of reusable deep learning components has grown over the various lessons. Think about the various pieces you used in this lesson to build your state of the art (almost) ResNet9 deep learning architecture for image classification.
max pooling layer
fully connected layer
Need to review something from the previous lessons in the course.
You can access the first lesson here.
You can access the second lesson here.
You can access the third lesson here.
You can access the 4th lesson here.
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