paulhendricks

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

paulhendricks

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft

paulhendricks

Latency Comparison Numbers
--------------------------
L1 cache reference/hit                       1.5 ns                      4 cycles
Floating-point add/mult/FMA operation        1.5 ns                      4 cycles
L2 cache reference/hit                       5   ns                      12 ~ 17 cycles
Branch mispredict                            6   ns                      15 ~ 20 cycles
L3 cache hit (unshared cache line)          16   ns                      42 cycles
L3 cache hit (shared line in another core)  25   ns                      65 cycles
Mutex lock/unlock                           25   ns
L3 cache hit (modified in another core)     29   ns                      75 cycles

paulhendricks

#!/usr/bin/env julia

using Luxor

Drawing(500, 500, "/tmp/julia-flowers.png")
background("white")

function juliaflowers(cpos::Point, radius; petals=30, outercircleratio=0.75, innercircleratio=0.65)
    # clockwise, from bottom LEFT...
    points3 = ngon(cpos, radius, 3, pi/6, vertices=true)

paulhendricks

import numpy as np
import pymc
import pdb

def unconditionalProbability(Ptrans):
   """Compute the unconditional probability for the states of a
   Markov chain."""

   m = Ptrans.shape[0]

paulhendricks

innovationScore,innovationScore2,names,type,description
4.7,470,3L Labs Footlogger,medical,"The 3L Labs Footlogger is a wearable fitness tracking device that aims at spotting health problems early, as well as logging daily activity. Placed in the user's shoe, 8 sensors coupled to 1 accelerometer help identify and record exercise habits. The data is then disclosed to the user via text or smartphone app. This computing device's technology can be used for athletes training, regular everyday workouts and rehabilitation."
5.44,544,4D Force,,"The 4D Force is a wearable technology that detects brain waves and converts them into electric signals. 4D Force developed a platform that can capture and compute high quality EEG/ EOG/EMG signals. With the device, users can control games by using the power of their thoughts. 4D Force can also be used for medical purposes as it has the ability to interpret electrical signals generated by the body, and create recommendations for changes in lifestyle."
4,400,4iiii Viiiiva,Fitnes

paulhendricks

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

paulhendricks

paulhendricks

from random import randint

def create_population(create, pop_size=1000):
    """Create population list"""
    return [create() for i in xrange(pop_size)]

def crossover(combine, population, pop_size):
    """Increase population size by combining"""
    original_size = len(population)
    while len(population) < pop_size:

paulhendricks

install.packages('caret')
install.packages('ccd')
install.packages('d3Network')
install.packages('data.table')
install.packages('dplyr')
install.packages('DMwR')
install.packages('e1071')
install.packages('ergm')
install.packages('ff')
install.packages('foreach')