Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
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Analyzing sequential data using recurrent neural networks

With all our neural network examples so far, we have been using static data. Neural networks can also be used effectively to build models that process sequential data. Recurrent neural networks (RNNs) are great at modeling sequential data. You can learn more about recurrent neural networks at:

When we are working with time series data, we normally cannot use generic learning models. We need to capture the temporal dependencies in the data so that a robust model can be built. Let's see how to build it.

Create a new Python file and import the following packages:

import numpy as np
import matplotlib.pyplot as plt
import neurolab as nl

Define a function to generate the waveforms. Start by defining four sine waves:

def get_data(num_points):
    # Create sine waveforms
    wave_1 = 0.5 * np.sin(np.arange(0, num_points))