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https://www.dataquest.io/blog/basic-statistics-with-python-descriptive-statistics/
https://blogs.oracle.com/ai/types-of-machine-learning-and-top-10-algorithms-everyone-should-know
https://www.kdnuggets.com/2018/05/5-reasons-logistic-regression-first-data-scientist.html
https://www.kdnuggets.com/2018/02/logistic-regression-concise-technical-overview.html
https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861
https://machinelearningmastery.com/a-gentle-introduction-to-the-central-limit-theorem-for-machine-learning/
##################################################################################################
# experiments service
##################################################################################################
apiVersion: v1
kind: Service
metadata:
labels:
service: ambassador
name: ambassador
annotations:
@karlmutch
karlmutch / waya-dl-setup.sh
Created November 21, 2017 21:57 — forked from mjdietzx/waya-dl-setup.sh
Install CUDA Toolkit v8.0 and cuDNN v6.0 on Ubuntu 16.04
#!/bin/bash
# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
apt-get update
apt-get install cuda-nvrtc-8-0 cuda-nvgraph-8-0 cuda-cusolver-8-0 cuda-cublas-8-0 cuda-cufft-8-0 cuda-curand-8-0 cuda-cusparse-8-0 cuda-npp-8-0 cuda-cudart-8-0 cuda-drivers
nvidia-smi
sudo rmmod nvidia_drm
sudo rmmod nvidia_modeset
sudo rmmod nvidia_uvm
sudo rmmod nvidia
nvidia-smi
@karlmutch
karlmutch / get_available_gpus.py
Created November 9, 2017 00:05 — forked from jovianlin/get_available_gpus.py
Get List of Devices in TensorFlow
from tensorflow.python.client import device_lib
def get_available_gpus():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU']
get_available_gpus()
@karlmutch
karlmutch / postgres_array.go
Created April 12, 2017 21:35 — forked from adharris/postgres_array.go
PostgreSQL demo of Array types using Golang
package main
import (
"database/sql"
"errors"
"fmt"
_ "github.com/bmizerany/pq"
"os"
"regexp"
"strings"

Keybase proof

I hereby claim:

  • I am karlmutch on github.
  • I am karlmutch (https://keybase.io/karlmutch) on keybase.
  • I have a public key ASBi1fSM4ozPiy_0S-Cus0CNsEIiKaS4pNfKLBgWgvVf4Ao

To claim this, I am signing this object:

// string and rune
const sample = "\xbd\xb2\x3d\xbc\x20\xe2\x8c\x98"
fmt.Printf("%x\n", sample) "bdb23dbc20e28c98"
fmt.Printf("% x\n", sample) "bd b2 3d bc 20 e2 8c 98"
fmt.Printf("%q\n", sample) "\xbd\xb2=\xbc ⌘"
fmt.Printf("%+q\n", sample) "\xbd\xb2=\xbc \u2318"
const nihongo = "日本語"
package main
import "fmt"
// fibonacci is a function that returns
// a function that returns an int.
func fibonacci() func() int {
accum1 := 1
accum2 := 0
return func() int {
func WordCount(s string) map[string]int {
wcMap := make(map[string]int)
for _,word := range strings.Fields(s) {
if _,ok := wcMap[word] ; ok {
wcMap[word]++
} else {
wcMap[word] = 1
}
}