Lesson 1 In-Class Discussion

! pip install isoweek

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what about cv2 i get its not found

Add these lines to requirements.txt and do pip install -r requirements.txt

isoweek>=1.3.3
pandas_summary>=0.0.41
torchtext>=0.2.0

Edited to fix pandas_summary. Thanks @TheLariat!

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Pre-prosessing image data is a very particular. Also, every model needs a slightly different pre-processing. That is why it is done by the library.

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​conda install -c conda-forge opencv

cv2 refers to openCV… you can install via conda or build from source

pip install opencv python

conda install opencv

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Hey there. Going out of memory on my 970. Any guidance on how to approach this with this fastai library?

Are we going to cover cyclic learning rates in this course, or leave it for part 2?

My intuition is that:

Traditionally in ML, we have been coming up with our own approximate function (the model) that we think would mimic the function we are trying to predict. The shortcoming is that the approximate function we come up, is based on the best of our knowledge and it does not have the freedom to change it’s form. It’s form is fixed. For e.g. if we think the model for a particular problem should be f(x) = w1x^2 + w2x+b, the form remains so and never changes.

In deep learning, by stacking weighted sum of inputs and non-linear activation functions, we let the network figure out by itself what form the approximate function should be - it does so by adjusting it’s own weights. By adding more layers and nodes, and by choosing the right activation function, we give the network enough wiggle room to approximate any function -
given the loss function.

Makes sense?

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@jeremy How about if we have three classes unlike in class example in which probability of cat is close to 0 and for dog to 1. Then how can we detect which probability belong to which of three classes.

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Lowering the mini-batch size.

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Hey guys, how can I install cv2?

huh, I guess that question got answered quick …

Jeremy will show you how to do it.

@jeremy In the lecture you mention that you prefer to build a model first and then analyze the results to clean the data. This approach sounds good for images (and maybe audio and text too).
What about data which is just a bunch of features? Is it better to do the reverse approach in this case too?

I get this error when I launched my jupyter notebook.

Notebook validation failed
The save operation succeeded, but the notebook does not appear to be valid. The validation error was:

Notebook Validation failed: {u'model_id': u'43a3aea4b93c41128be460941019c404', u'version_minor': 0, u'version_major': 2} is not valid under any of the given schemas:
{
 "model_id": "43a3aea4b93c41128be460941019c404", 
 "version_minor": 0, 
 "version_major": 2
}

What should I correct in order to get this right?

install via conda or build from source

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Good, thanks! Was asking since there’s no “clear” parameter this time around for this :slight_smile: