This Review gives an overview of intersting stuff I stumbled over which are related to machine learning.
New Developments ¶
- KittiSeg (reddit): A toolkit for semantic segmentation based on TensorVision
- AudioSet: A dataset for accoustic events
Publications ¶
- Evolution Strategies as a Scalable Alternative to Reinforcement Learning
- Controllable Text Generation
- Stopping GAN Violence: Generative Unadversarial Networks: Probably one of the funniest ML things I've seen so far. Reminds me of Machine Learning A Cappella - Overfitting Thriller
- Deep Neural Networks Do Not Recognize Negative Images
- Twitter100k: A Real-world Dataset for Weakly Supervised Cross-Media Retrieval
- MINC-2500 dataset
- Second-order Convolutional Neural Networks
Software ¶
Interesting Questions ¶
- How to predict an item's category given its name?
- How do you share models?
- How many FLOPs does tanh need?
Miscallenious ¶
Color Maps ¶
Color Maps are important for visualizing data. But the default color map for many applications is jet, which is bad for several reasons:
- It's hard to estimate distances from jet
- Doesn't work well when printed in grayscale
- Even worse if you are colorblind
The YouTube clip A Better Default Colormap for Matplotlib
by Nathaniel Smith and Stéfan van der Walt gives a short introduction into
color theory. They introduce colorspacious
and viscm. viscm is a tool for
creating new color maps. They created viridis as a better alternative to
jet.
A blog post with roughly the same content is at bids.github.io/colormap. This is the default for matplotlib 2.0. If you wonder which matplotlib version you have:
$ python -c "import matplotlib;print(matplotlib.__version__)"
That is how you update matplotlib:
$ sudo -H pip install matplotlib --upgrade
Here is a list of other matplotlib colormaps:
['Accent', 'afmhot', 'autumn', 'binary', 'Blues', 'bone', 'BrBG', 'brg',
'BuGn', 'BuP', 'bwr', 'CMRmap', 'cool', 'coolwarm', 'copper', 'cubehelix',
'Dark2', 'flag', 'gist_earth', 'gist_gray', 'gist_heat', 'gist_ncar',
'gist_rainbow', 'gist_stern', 'gist_yarg', 'GnB', 'gnuplot', 'gnuplot2',
'gray', 'Greens', 'Greys', 'hot', 'hsv', 'jet', 'nipy_spectral', 'ocean',
'Oranges', 'OrRd', 'Paired', 'Pastel1', 'Pastel2', 'pink', 'PiYG', 'PRGn',
'prism', 'PuB', 'PuBuGn', 'PuOr', 'PuRd', 'Purples', 'rainbow', 'RdB', 'RdGy',
'RdP', 'RdYlB', 'RdYlGn', 'Reds', 'seismic', 'Set1', 'Set2', 'Set3',
'Spectral', 'spectral', 'spring', 'summer', 'terrain', 'Vega10', 'Vega20',
'Vega20b', 'Vega20c', 'winter', 'Wistia', 'YlGn', 'YlGnB', 'YlOrBr', 'YlOrRd']
Finally, some interesting links:
Class distribution ¶
You should always know if your data is severly unevenly distributed. Here is a little script to visualize the data distribution:
import matplotlib.pyplot as plt
data = y.flatten() # your labels
plt.hist(data, bins=np.arange(data.min(), data.max() + 2)) # yes, +2.
plt.show()
or
import seaborn as sns
data = y.flatten() # your labels
sns.distplot(data)
sns.plt.show()
For the CIFAR100 training data, this is pretty boring:
