calvingiles

def specialist_function_factory(func, *s_args, **s_kwargs):
    def specialist_func(*args, **kwargs):
        all_args = s_args + args
        all_kwargs = dict(list(s_kwargs.items()) + list(kwargs.items()))
        return func(*all_args, **all_kwargs)
    return specialist_func

calvingiles

Theming Slack for OSX

So, you love Slack, but you hate applications with large white backgrounds? Why not use Dark Mode!

Unfortunately, Slack does not have a Dark Mode, although it's on their list of possibilities.

But, don't fret - there is a solution! Because the slack native desktop apps are just wrappers around a web app, we can inject our own CSS to customize the application to our liking.

How to (OSX Only)

calvingiles

Blogs

• Sapphire Deep Learning Upskilling by Dr. Adam Bermingham, Dr. Ana Peleteiro Ramallo - 4 Jan 2017

Papers

• Learning Deep Architectures for AI by Yoshua Bengio
• Natural Language Processing (Almost) from Scratch by Ronan Collobert, 2011
• Recurrent neural network based language model by Toma´s Mikolov, 2010
• Distributed Representations of Words and Phrases and their Compositionality by Tomas Mikolov

Code

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class Graph(object):
    def __init__(self, nodes, edges=None):
        self.nodes = nodes
        self.edges = set()
        self.add_edges(edges or set())

    def __hash__(self):
        return hash(frozenset(self.edges)) + hash(frozenset(self.nodes))
    
    def __repr__(self):

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notebooks:
    command: echo created
    image: busybox
    volumes:
        - "~/Google Drive/notebooks:/notebooks"
data:
    command: echo created
    image: busybox
    volumes:
        - "~/Google Drive/data:/data"

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"""
>>> I = ai(start=0, step=1)
>>> I()
0
>>> I()
1
>>> J = ai(start=0, step=1)
>>> J()
0
>>> I()

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"""
>>> I = ai(start=0, step=1)
>>> I()
0
>>> I()
1
>>> J = ai(start=0, step=1)
>>> J()
0
>>> I()

calvingiles

# Auto-incremented number, starting at 0. 
# Relies on function defualts being evaluated only once and lists being mutable.
def ipp(__i=[-1]):
    '''This is a dirty hack'''
    __i[0] += 1
    return __i[0]

calvingiles

def GCD(a, b):
    if b:
        return GCD(b, a % b)
    return a

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library(stats)
library(ggplot2)
set.seed(1)

# for our first example, let's create some synthetic easy-to-cluster data
d <- data.frame()
d <- rbind(d, data.frame(x=1 + rnorm(20, 0, 0.1), y=1 + rnorm(20, 0, 0.1), label=as.factor(rep(1, each=20))))
d <- rbind(d, data.frame(x=1 + rnorm(20, 0, 0.1), y=3 + rnorm(20, 0, 0.1), label=as.factor(rep(2, each=20))))
d <- rbind(d, data.frame(x=3 + rnorm(20, 0, 0.1), y=1 + rnorm(20, 0, 0.1), label=as.factor(rep(3, each=20))))
d <- rbind(d, data.frame(x=3 + rnorm(20, 0, 0.1), y=3 + rnorm(20, 0, 0.1), label=as.factor(rep(4, each=20))))