CristinaSolana

1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

mitchwongho

# Assuming an Ubuntu Docker image
$ docker run -it <image> /bin/bash

cburgdorf

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense

training_data = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
target_data = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(32, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

mjdietzx

#!/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

geohot

def ransac_polyfit(x, y, order=3, n=20, k=100, t=0.1, d=100, f=0.8):
  # Thanks https://en.wikipedia.org/wiki/Random_sample_consensus
  
  # n – minimum number of data points required to fit the model
  # k – maximum number of iterations allowed in the algorithm
  # t – threshold value to determine when a data point fits a model
  # d – number of close data points required to assert that a model fits well to data
  # f – fraction of close data points required
  
  besterr = np.inf

nex3

The PATH is an important concept when working on the command line. It's a list of directories that tell your operating system where to look for programs, so that you can just write script instead of /home/me/bin/script or C:\Users\Me\bin\script. But different operating systems have different ways to add a new directory to it:

Windows

1. The first step depends which version of Windows you're using:

• If you're using Windows 8 or 10, press the Windows key, then search for and

ihoromi4

def seed_everything(seed: int):
    import random, os
    import numpy as np
    import torch
    
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)

markshust

# Add the following records to your DNS config
## Mac: /etc/hosts
## Windows: C:\Windows\System32\drivers\etc\hosts
104.18.23.110 admin-stg.notion.so
104.18.23.110 aif.notion.so
104.18.23.110 analytics-iframe.notion.so
104.18.23.110 analytics.pgncs.notion.so
104.18.23.110 api.notion.so
104.18.23.110 api.pgncs.notion.so
104.18.23.110 dev.notion.so