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Neural Network Projects with Python

You're reading from   Neural Network Projects with Python The ultimate guide to using Python to explore the true power of neural networks through six projects

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Length 308 pages
Edition 1st Edition
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Author (1):
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James Loy James Loy
Author Profile Icon James Loy
James Loy
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Table of Contents (10) Chapters Close

Preface 1. Machine Learning and Neural Networks 101 FREE CHAPTER 2. Predicting Diabetes with Multilayer Perceptrons 3. Predicting Taxi Fares with Deep Feedforward Networks 4. Cats Versus Dogs - Image Classification Using CNNs 5. Removing Noise from Images Using Autoencoders 6. Sentiment Analysis of Movie Reviews Using LSTM 7. Implementing a Facial Recognition System with Neural Networks 8. What's Next? 9. Other Books You May Enjoy

Exploratory data analysis

Let's dive right into the dataset. The instructions to download the NYC taxi fares dataset can be found in the accompanying GitHub repository for the book (refer to the Technical requirements section). Unlike in the previous chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons, we're not going to import the original dataset of 55 million rows. In fact, most computers would not be able to store the entire dataset in memory! Instead, let's just import the first 0.5 million rows. Doing this does have its drawbacks, but it is a necessary tradeoff in order to use the dataset in an efficient manner.

To do this, run the read_csv() function with pandas:

import pandas as pd

df = pd.read_csv('NYC_taxi.csv', parse_dates=['pickup_datetime'], nrows=500000)
The parse_dates parameter in read_csv allows pandas to easily...
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