jxnl

jxnl

About myself, and work history

Founded Data Hackers, Data Science Club w/ Kevin Huang and Anni Dong.

• Sysomos
• Nyu
• Stitchfix
• Facebook

Disclaimer

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• Mentors
• Conceptual Heavyweights
• Trends visionaries
• New perspectives
• Client point person
• Key player in business development
• Questions everything - Designs Solutions Goals
• Push/Review Designers
• Risk Taking
• Moral

jxnl

2cents on diversity.

Some friends have asked me about my thoughts on google memo.

Stereotype threat is an observable phenomena. 

It doesn’t explain every performance gap, and eliminating it won’t close all performance gaps, however, it’s net loss and suboptimal.

tl;dr

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motivation

Variational Autoencoders (VAE) are extremely appealing as they allow for learning complicated distributions taking advantage of recent progress in gradient descent algorithms and accelerated processing with GPUs. The latent space of regular autoencoders are typically very sparse and unrestricted, making it difficult to generate data robust to variations of the latent variables. Traditional VAEs use normal priors regularize the latent space however these assuptions are unsound when it comes to complex data such as images or text.

challenge

We propose Dirichlet priors for a multinomial latent space. This latent space allows us to explore the data by interpreting $P(\theta_i)$ as the likelihood of belonging to a latent class, as a form of non-linear soft clustering.

contributions

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t = """
paste transcript
"""


grades = [x for x in t.split("\n\n") if "Y" in x][1:]
grades =[[x for x in y.strip().replace("   ", "_").replace("  ", "_").split("_") if x != ""][:4] for y in grades]
grades = [(x[0], x[-1]) for x in grades if len(x[-1]) == 2]
df = pd.DataFrame(grades, columns=["course_code", "grade"])

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log - loading features
feature:  train
feature:  p.h.building
feature:  p.m.building
feature:  p.l.building
feature:  p.h.manager
feature:  p.m.manager
feature:  p.l.manager
feature:  n_photos
feature:  latitude

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# coding: utf-8

# # Loading and Transforming json data

# In[1]:

import pandas as pd
import numpy as np
import pickle

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import mechanize
import pandas as pd
import os

from bs4 import BeautifulSoup 
from IPython.core.display import display, HTML

br = mechanize.Browser()

br.open("http://www.adm.uwaterloo.ca/infocour/CIR/SA/under.html")

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def simpson(a, b, f, N):
    return (1.0 / 3.0) * (2 * (((b - a) / N) * sum(f(v) for v in [i * ((b - a) \
    / (2. * N)) for i in range(2 * N + 1)][1:2 * N + 1:2])) + (((b - a) / N) * \
    ((f(a) + f(b)) / 2.0 + sum (f(v * ((b - a) / N) + a) for v in xrange(1, N)))
    ))