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Applied Deep Learning with Python

You're reading from   Applied Deep Learning with Python Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

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Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781789804744
Length 334 pages
Edition 1st Edition
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Authors (2):
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Alex Galea Alex Galea
Author Profile Icon Alex Galea
Alex Galea
Luis Capelo Luis Capelo
Author Profile Icon Luis Capelo
Luis Capelo
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Introduction to Neural Networks and Deep Learning

The MNIST dataset does not contain numbers on the edges of images. Hence, neither network assigns relevant values to the pixels located in that region. Both networks are much better at classifying numbers correctly if we draw them closer to the center of the designated area. This shows that neural networks can only be as powerful as the data that is used to train them. If the data used for training is very different than what we are trying to predict, the network will most likely produce disappointing results. In this chapter, we will cover the basics of neural networks and how to set up a deep learning programming environment. We will also explore the common components of a neural network and its essential operations. We will conclude this chapter by exploring a trained neural network created using TensorFlow.

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