Wiki: Lesson 1

Part 1 (2018)
105

Fastai Part 1 v2 Lesson 1 Timelines, done.
@jeremy @hiromi

Video timelines for Lesson 1

  • 00:00:01 Welcome to Part 1, Version 2 of “Practical Deep Learning for Coders”,
    Check the Fastai community for help on setting up your system on “forums.fast.ai”

  • 00:02:11 The “Top-Down” approach to study, vs the “Bottom-Up”,
    Why you want a nVidia GPU (Graphic Processing Unit = a video card) for Deep Learning

  • 00:04:11 Use crestle.com if you don’t have a PC with a GPU.

  • 00:06:11 Use paperspace.com instead of crestle.com, for faster and cheaper GPU computing. Technical hints to make it work with a Jupyter Notebook.

  • 00:12:30 Start with Jupyter Notebook lesson1.ipynb ‘Dogs vs Cats’

  • 00:20:20 Our first model: quick start.
    Running our first Deep Learning model with the ‘resnet34’ architecture, epoch, accuracy on validation set.

  • 00:24:11 “Analyzing results: looking at pictures” in lesson1.ipynb

  • 00:30:45 Revisiting Jeremy & Rachel’s approach of “Top-Down vs Bottom-Up” teaching philosophy, in details.

  • 00:33:45 Explaining the “Course Structure” of Fastai, with a slide showing its 8 steps.
    Looking at Computer Vision, then Structured Data (or Time Series) with the Kaggle Rossmann Grocery Sales competition, then NLP (Natural Language Processing), then Collaborative Filtering for Recommendation Systems, then Computer Vision again with ResNet.

  • 00:44:11 What is Deep Learning ? A kind of Machine Learning.

  • 00:49:11 The Universal Approximation Theorem, and examples used by Google corporation.

  • 00:58:11 More examples using Deep Learning, as shown in the PowerPoint from Jeremy course in ML1 (Machine Learning 1)
    What is actually going on in a Deep Learning model, with convolutional network.

  • 01:02:11 Adding a Non-Linear Layer to our model, sigmoid or ReLu (rectified linear unit), SGD (Stochastic Gradient Descent)

  • 01:08:20 A paper on “Visualizing and Understanding Convolutional Networks”, implementation on ‘lesson1.ipynb’, ‘cyclical learning rates’ with Fastai library as “lr_find” or learning rate finder.
    Why it starts training a model but stops before 100%: use Learner Schedule Finder.

  • 01:21:30 Why you need to use Numpy and Pandas libraries with Jupyter Notebook: hit ‘TAB’ for more info, or “Shift-TAB” once or twice or thrice (three times) to bring up the documentation for the code.
    Enter ‘?’ before the function, or ‘??’ to look at the code in details.

  • 01:24:40 Using the ‘H’ shortcut in Jupyter Notebook, to see the Keyboard Shortcuts.

  • 01:25:40 Don’t forget to turn off your session in Crestle or Paperspace, or you end up being charged.

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