ebraraktas

# Example command:
# python copy_headers.py --headers tensorflow/lite/model.h tensorflow/lite/interpreter.h \
# --destination MyAwesomeProject/include
# --include-dirs ../flatbuffers/include  # Note that these are relative to --source-dir
# --source-dir ../tensorflow/

import argparse
import os
import subprocess

arturo182

<!--XSL style sheet to convert EESCHEMA XML Partlist Format to grouped CSV BOM Format
    Copyright (C) 2014, Wolf Walter.
    Copyright (C) 2013, Stefan Helmert.
    Copyright (C) 2018, Kicad developers.
    Copyright (C) 2019, arturo182.
    GPL v2.

	Functionality:
		Generation of JLCPCB PCBA compatible BOM

bogdan-kulynych

# WARNING: These steps seem to not work anymore!

#!/bin/bash

# Purge existign CUDA first
sudo apt --purge remove "cublas*" "cuda*"
sudo apt --purge remove "nvidia*"

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

webhive

#ifndef BLE400_H
#define BLE400_H

// LEDs definitions for BLE400
#define LEDS_NUMBER    5

#define LED_START      18
#define LED_1          18
#define LED_2          19
#define LED_3          20

akirasosa

import CoreML
import XCTest
@testable import mlsample

class mlsampleTests: XCTestCase {
    
    override func setUp() {
        super.setUp()
    }
    

nieldeokar

package com.example.nileshdeokar.simplevibratedemo;

import android.os.Build;
import android.os.VibrationEffect;
import android.os.Vibrator;
import android.support.annotation.RequiresApi;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.widget.Toast;
/*

StanislawAntol

#!/usr/bin/python3

from sys import exit
from sys import stdout
from sys import path as syspath
from os import path as osp
from os import stat, makedirs
import argparse

from shutil import copyfile

denisb411

import pyaudio
import numpy as np
import time
import wave
import matplotlib.pyplot as plt


# open stream
FORMAT = pyaudio.paInt16
CHANNELS = 1

wassname

"""
Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss.
It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy
"""

# define custom loss and metric functions 

from keras import backend as K

def dice_coef(y_true, y_pred, smooth=1):

karpathy

""" 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