Thanks @alessa! This was helpful for a general train / test / split. I was wondering more specifically -
do you know of any specific setup requirements for the train / test / split for fastAi’s method?
Thanks again.
When choosing a learning rate with the LR finder, you can plot a vertical line to ensure you choose the correct x-coordinate of the point you’re interested in. Otherwise it can be difficult to interpret values on the x-axis, since they’re in log scale.
import matplotlib.pyplot as plt
learn.sched.plot()
plt.axvline(x=1.6e-2, color="red");
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Also adding %matplotlib notebook seems awesome (edit the same plot until created a new one)
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Kaggle CatsDogs Redux Kernel competition asks us to report whats the probability of that image to be a dog.hence interested in calculating dog probability
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Good idea
I thought some one had created a timeline for this lesson, but I can’t find it - am I imagining things? @hiromi @EricPB where did we get to with this for the new version of the video? Sorry for my poor memory!
I believe there was one for the original lesson 1 video, but I don’t recall one for the re-taped version. I can certainly create one 
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That would be quite wonderful! I’ve nearly finished the new course web site and suddenly discovered we don’t have a timeline! 
@jeremy, question about V2 vs. Machine Learning For Coders: I’ve had significant dev experience and want to complete one of these, do you have a suggestion of which course to take? Is Machine Learning For Coders ready for the public? I’ve started both of the first videos of the respective courses. Thanks.
Master @Jeremy,
You indeed found the nasty secret in the “Video Timelines for Part 1 V2”: there is none existing so far for Lesson 1, but Lessons 2 to 7 are covered with the help of your humble servants here (hiromi was super efficient/fast at fixing my mistakes on L7) 
I will work on it tomorrow/this weekend.
Did anyone mention that he/she was looking for your notebook on Favorita Comp, including Preds and Submission, now that it’s over ? 
They’re both ready. Perhaps read the experiences of other students on the forum and see what you think. They’re both worth doing.
You and @hiromi are both very kind
I’m rather embarrassed that I never got around to creating a groceries model that I’m actually happy with. But I’ll endeavor to dig up my notebook after I get this course out…
@EricPB, I’m at a hackathon tonight so I’ll see how far I can get during my breaks. You can make it prettier for me when you get a chance
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I will do that @hiromi, things are a bit hectic here in Stockholm with family and the Recruit Visitor Forecast Kaggle competition.
But I’m sure you and I can build a basic Lesson 1 Video Timeline that we can improve as it goes, just like you made it happen for Lesson 7.
@jeremy: we don’t expect your Favorita notebook to reach Top 3 positions like Rossmann, just that some of us had trouble using the Fastai library to move from Training (Check !) to Predicting/Submitting a CSV (Fail !). 
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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.
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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.
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00:12:30 Start with Jupyter Notebook lesson1.ipynb ‘Dogs vs Cats’
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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.
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00:49:11 The Universal Approximation Theorem, and examples used by Google corporation.
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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)
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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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Yay! Perfect timing
ImportError Traceback (most recent call last)
in ()
----> 1 import utils; reload(utils)
2 from utils import plots
D:\fast.ai\courses-master\deeplearning1\nbs\utils.py in ()
16 from numpy import newaxis
17 import scipy
—> 18 from scipy import misc, ndimage
19 from scipy.ndimage.interpolation import zoom
20 from scipy.ndimage import imread
~\Anaconda3\envs\kj\lib\site-packages\scipy\misc_init_.py in ()
51 from .common import *
52 from numpy import who, source, info as _info
—> 53 from scipy.interpolate._pade import pade
54 from scipy.special import comb, factorial, factorial2, factorialk, logsumexp
55
~\Anaconda3\envs\kj\lib\site-packages\scipy\interpolate_init_.py in ()
174 from future import division, print_function, absolute_import
175
–> 176 from .interpolate import *
177 from .fitpack import *
178
~\Anaconda3\envs\kj\lib\site-packages\scipy\interpolate\interpolate.py in ()
19
20 import scipy.linalg
—> 21 import scipy.special as spec
22 from scipy.special import comb
23
~\Anaconda3\envs\kj\lib\site-packages\scipy\special_init_.py in ()
638 from .sf_error import SpecialFunctionWarning, SpecialFunctionError
639
–> 640 from ._ufuncs import *
641
642 from .basic import *
ImportError: DLL load failed: The specified module could not be found.
@jkashish18 that’s not enough information for anyone to be able to help you. In fact, spending no time at all telling us what you’re doing, what you’ve tried, etc makes it look like you’re investing far less time on your problem than you’re asking other people to spend helping you. You may find these tips useful: http://wiki.fast.ai/index.php/How_to_ask_for_Help
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Just wondering, I noticed that there was more content in the Lesson 1 IPython Notebook that was not covered in the video like Finetuning, analyzing results, the confusion matrix, etc. Are we expected to cover this on our own or will this be covered later?