2. Real-World Deep Learning: Predicting the Price of Bitcoin

Activity 2.01: Assembling a Deep Learning System

Solution:

We will continue to use Jupyter Notebooks and the data prepared in previous exercises of chapter 2 (data/train_dataset.csv), as well as the model that we stored locally (bitcoin_ lstm_v0.h5):

1. Import the libraries required to load and train the deep learning model:

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot as plt
   %matplotlib inline
   from tensorflow.keras.models import load_model
   plt.style.use('seaborn-white')

   Note

   The close_point_relative_normalization variable will be used to train our LSTM model.

   We will start by loading the dataset we prepared during our previous activities. We'll use pandas to load that dataset into memory.

2. Load the training dataset into memory using pandas, as follows:

   train = pd.read_csv('data/train_dataset.csv')

3. Now, quickly inspect the dataset by executing the following command...