Book Image

The Deep Learning Workshop

By : Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So
Book Image

The Deep Learning Workshop

By: Mirza Rahim Baig, Thomas V. Joseph, Nipun Sadvilkar, Mohan Kumar Silaparasetty, Anthony So

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)

5. Deep Learning for Sequences

Activity 5.01: Using a Plain RNN Model to Predict IBM Stock Prices


  1. Import the necessary libraries, load the .csv file, reverse the index, and plot the time series (the Close column) for visual inspection:
    import pandas as pd, numpy as np
    import matplotlib.pyplot as plt
    inp0 = pd.read_csv("IBM.csv")
    inp0 = inp0.sort_index(ascending=False)
    inp0.plot("Date", "Close")

    The output will be as follows, with the closing price plotted on the Y-axis:

    Figure 5.40: The trend for IBM stock prices

  2. Extract the values for Close from the DataFrame as a numpy array and plot them using matplotlib:
    ts_data = inp0.Close.values.reshape(-1,1)

    The resulting trend is as follows, with the index plotted on the X-axis:

    Figure 5.41: The stock price data visualized

  3. Assign the final 25% data as test data and the first 75% as train data:
    train_recs = int(len(ts_data...