Daniel Mahler mhlr

## poisson_factorization_machine.py
""" Poisson-loss Factorization Machines with Numba

Follows the vanilla FM model from:
Steffen Rendle (2012): Factorization Machines with libFM.
In: ACM Trans. Intell. Syst. Technol., 3(3), May.
http://doi.acm.org/10.1145/2168752.2168771

See also: https://github.com/coreylynch/pyFM
"""

## springer-free-maths-books.md

      
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                Springer made a bunch of books available for free, these were the direct links
              
          
    These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

  
## init.el
(if window-system
    (tool-bar-mode -1)
  (menu-bar-mode -1))
(when (eq window-system 'ns)
  (add-to-list 'default-frame-alist '(height . 80))
  (add-to-list 'default-frame-alist '(width . 132)))

(require 'package)
(setq package-enable-at-startup nil)
(add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/"))

## Examining Your Presence on Twitter with Python.ipynb

      
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                / Examining Your Presence on Twitter with Python.ipynb
            
            
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              March 24, 2016 02:48
            
              
                http://kldavenport.com/examining-your-presence-on-twitter-with-python
              
          
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## anonymous

      
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                / bayesian_neural_network.ipynb
            
            
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## SimpleNeuralNets.ipynb

      
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                Notes for "Why does deep and cheap learning work so well?" (ArXiv:1608.08225v1/cond-mat.dis-nn) by Lin and Tegmark.
              
          
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## render_env.py
import json
import glob
import os
from os.path import join, basename
# install this with "conda install -c conda-forge python-graphviz"
import graphviz as gv

# path to your conda environment
path = os.environ.get('CONDA_PREFIX')
if path is None:

## scaling-gcs.ipynb

      
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## how-to-make-a-racist-ai-without-really-trying.ipynb

      
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## VegaMagic.ipynb

      
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                Simple Vega/Vega-Lite magic function
              
          
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	""" Poisson-loss Factorization Machines with Numba

	Follows the vanilla FM model from:
	Steffen Rendle (2012): Factorization Machines with libFM.
	In: ACM Trans. Intell. Syst. Technol., 3(3), May.
	http://doi.acm.org/10.1145/2168752.2168771

	See also: https://github.com/coreylynch/pyFM
	"""
	(if window-system
	(tool-bar-mode -1)
	(menu-bar-mode -1))
	(when (eq window-system 'ns)
	(add-to-list 'default-frame-alist '(height . 80))
	(add-to-list 'default-frame-alist '(width . 132)))

	(require 'package)
	(setq package-enable-at-startup nil)
	(add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/"))
	import json
	import glob
	import os
	from os.path import join, basename
	# install this with "conda install -c conda-forge python-graphviz"
	import graphviz as gv

	# path to your conda environment
	path = os.environ.get('CONDA_PREFIX')
	if path is None: