# Kaggler 0.3.7 Released

Changes:

• Cython optimization for performance – boundscheck(False), wraparound(False), and cdivision(True) are used.
• Adaptive learning rate – instead of $\frac{1}{\sqrt{n_i} + 1}$, $\frac{1}{\sqrt{\sum{g_i^2}} + 1}$ is used where $g_i$ is the gradient of the associated weight.
• Type correction – change the type of index from double to int.

You can upgrade Kaggler either by using pip:
 $(sudo) pip install -U Kaggler  or from the source at github: $ git fetch origin $git rebase origin/master$ python setup.py build_ext --inplace \$ (sudo) python setup.py install 

I haven’t had a chance to use it with real competition data yet – after the Avazu competition, I deleted whole build directory 🙁 – and I don’t have numbers for how much faster (or slower?!) it becomes after these changes yet.

I will jump into another competition soon, and let you know how it works. 🙂

## Author: jeongyoonlee

Kaggler. Data Scientist.