wbadry

public class BoyerMoore
    {
        private int[]  _jumpTable;
        private byte[] _pattern;
        private int    _patternLength;
        public BoyerMoore()
        {
        }
        public BoyerMoore(byte[] pattern)
        {

wbadry

import numpy as np
import matplotlib.pyplot as plt


def gradientdescentoptimizer(param, c, learning_rate =0.01):
    # Forward Propagation
    param["cost"] = c[0][0] * param["w"] ** 2 + c[1][0] * param["w"] + c[2][0]
    # Backward Propagation
    param["dw"] = 2 * c[0][0] * param["w"] + c[1][0]
    # Update Omega

wbadry

# imports
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Create linear spaced million points
xData = np.linspace(0.0, 10.0, 1000000, dtype=float)
# Create million random points
noise = np.random.randn(len(xData))

wbadry

#include <Servo.h>
#include <Wire.h>
#include <Firmata.h>

#define I2C_WRITE                   B00000000
#define I2C_READ                    B00001000
#define I2C_READ_CONTINUOUSLY       B00010000
#define I2C_STOP_READING            B00011000
#define I2C_READ_WRITE_MODE_MASK    B00011000
#define I2C_10BIT_ADDRESS_MODE_MASK B00100000