Data preparation

In this section, we are going to perform data preparation. We have to prepare the data before running a deep learning model. 

We will start by exploring the data. Later, we will apply the computer vision techniques that we studied in Chapter 4, Computer Vision for Self-Driving Cars. Let's get started:

1. First, add the necessary libraries, including numpy, pandas, and keras, as shown in the following code block: 

import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Convolution2D, MaxPooling2D, Dropout, Flatten, Dense
import cv2
import pandas as pd
import random
import os
import ntpath
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import matplotlib.image as mpimg
from imgaug import augmenters as iaa

2. The next step is to initialize the folder name where the training images and driving logs will be stored, as well...