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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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Technical requirements

The Python libraries required for this chapter are as follows:

  • matplotlib 3.0.2
  • Keras 2.2.4
  • seaborn 0.9.0
  • scikit-learn 0.20.2

The code for this chapter can be found in the GitHub repository for the book.

To download the code onto your computer, you may run the following git clone command:

$ git clone https://github.com/PacktPublishing/Neural-Network-Projects-with-Python.git

After the process is complete, there will be a folder entitled Neural-Network-Projects-with-Python. Enter the folder by running the following:

$ cd Neural-Network-Projects-with-Python

To install the required Python libraries in a virtual environment, run the following command:

$ conda env create -f environment.yml

Note that you should have installed Anaconda on your computer first, before running this command. To enter the virtual environment, run the following command:

$ conda activate...

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