lpraat

import tensorflow as tf
from src.utils.dtypes import tf_float_type


def conj_gradient(A, b, iters):
    # todo can be optimized

    x = tf.zeros_like(b)
    r = b - tf.matmul(A, x)
    p = r

lpraat

import matplotlib.pyplot as plt
from matplotlib import animation
import numpy as np


fig = plt.figure()
ax = plt.axes(xlim=(-2,5), ylim=(0,10))
line, = ax.plot([], [])

lpraat

"""
Example showing that backward pass of conv layer can be done using transposed convolution
"""
import numpy as np

a_prev = np.array([
    [1,1,1,1],
    [2,2,2,2],
    [3,3,3,3],
    [4,4,4,4]

lpraat

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

from scipy.fftpack import fft
import matplotlib.animation as animation


CHUNK = 1024  # signal is split into CHUNK number of frames
FORMAT = pyaudio.paInt16

lpraat

1. Create virtualenv
2. source created_env/bin/activate
3. (created_env) pip install ipykernel
4. (created_env) ipython kernel install --user --name=created_env
5. jupyter kernelspec list
6. go in the folder of your newly created kernel and verify it correctly points to the python executable of the newly created
   environment(this can be seen in kernel.json file)

lpraat

def little_endian(hex_str):
    hex_str = fix_hex(hex_str)
    chars = [hex_str[i:i+2] for i in range(0, len(hex_str), 2)][::-1]
    return bytes.fromhex("".join(chars))

def remove0x(hex_str):
    return hex_str[2:]

def half(hex_str):
    hex_str = fix_hex(hex_str)

lpraat

import turtle
import sys


def update_maps(graph, turtle, color):
    graph[turtle_pos(turtle)] = color


def turtle_pos(turtle):
    return (round(turtle.xcor()), round(turtle.ycor()))

lpraat

#include <stdio.h>
#define ORDER 3
#define DIM ORDER*ORDER

int checkLine(int m[DIM][DIM], int line)
{
    int i, j;
    for (i = 0; i < DIM-1; i++)
        for (j = i+1; j < DIM; j++)
            if (m[line][i] == m[line][j] &&