saihttam

Before you start

Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:

docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

https://archive.org/download/archiveteam_coursera_20160627114043/coursera_20160627114043.megawarc.warc.gz

bigdata = Web Intelligence and Big Data
clinical skills = Teaching and Assessing Clinical Skills
comp finance = Introduction to Computational Finance and Financial Econometrics
data sci = Introduction to Data Science
dmathgen = 离散数学概论 Discrete Mathematics Generality
global introuslaw = The Global Student's Introduction to U.S. Law
global theatre = Theatre and Globalization
global theatre = Theatre and Globalization
inforiskman = Information Security and Risk Management in Context

The following attack will display a "you've been signed out" page for GMail, and attempt to steal your account credentials.

DO NOT PUT ANY ACCOUNT CREDENTIALS INTO ANY TABS CREATED AFTER VISITING THESE LINKS :)

I received an email in my GMail inbox with a fake attachment image, styled to look like the real GMail attachment UI:

This linked to a page that ended up displaying a fake "you've been signed out" link, via the data:text/html... URL feature of Chrome:

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

Quick List of Resources for Topological Data Analysis with Emphasis on Machine Learning

This is just a quick list of resourses on TDA that I put together for @rickasaurus after he was asking for links to papers, books, etc on Twitter and is by no means an exhaustive list.

Survey Papers

Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject

Topology and Data by Gunnar Carlsson
Barcodes: The Persistent Topology of Data by Robert Ghrist

Other Papers and Web Resources

Extracting insights from the shape of complex data using topology A good introductory paper in Nature on the Mapper algorithm.

	"""
	Simple example of manually performing "automatic" differentiation
	"""
	import numpy as np
	from numpy import exp, sin, cos


	def f(x, with_grad=False):
	# Need to cache intermediates from forward pass (might not use all of them).
	a = exp(x)

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	# Author: Jean-Remi King <[email protected]>
	"""
	Illustrate how a hinge loss and a log loss functions
	typically used in SVM and Logistic Regression
	respectively focus on a variable number of samples.
	For simplification purposes, we won't consider the
	regularization or penalty (C) factors.
	"""
	import numpy as np
	import matplotlib.animation as animation