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Neural Network Projects with Python

Neural Network Projects with Python

By : James Loy
4.6 (15)
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Neural Network Projects with Python

Neural Network Projects with Python

4.6 (15)
By: James Loy

Overview of this book

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
Table of Contents (10 chapters)
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Putting it all together

We have accomplished a lot in this chapter. Let's do a quick recap of the code that we have written so far.

We started off by defining a function for preprocessing. This preprocess function takes a DataFrame as an input and performs the following actions:

  • Removing missing values
  • Removing outliers in the fare amount
  • Replacing outliers in passenger count with the mode
  • Removing outliers in latitude and longitude (that is, only considering points within NYC)

This function is saved under utils.py in our project folder.

Next, we also defined a feature_engineer function for feature engineering. This function takes a DataFrame as an input and performs the following actions:

  • Creating new columns for year, month, day, day of the week, and hour
  • Creating new column for the Euclidean distance between the pickup and drop off points
  • Creating new columns for the...

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