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Hands-On Deep Learning Algorithms with Python

You're reading from   Hands-On Deep Learning Algorithms with Python Master deep learning algorithms with extensive math by implementing them using TensorFlow

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789344158
Length 512 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Deep Learning FREE CHAPTER
2. Introduction to Deep Learning 3. Getting to Know TensorFlow 4. Section 2: Fundamental Deep Learning Algorithms
5. Gradient Descent and Its Variants 6. Generating Song Lyrics Using RNN 7. Improvements to the RNN 8. Demystifying Convolutional Networks 9. Learning Text Representations 10. Section 3: Advanced Deep Learning Algorithms
11. Generating Images Using GANs 12. Learning More about GANs 13. Reconstructing Inputs Using Autoencoders 14. Exploring Few-Shot Learning Algorithms 15. Assessments 16. Other Books You May Enjoy

Implementing a CNN in TensorFlow

Now we will learn how to build a CNN using TensorFlow. We will use the MNIST handwritten digits dataset and understand how a CNN recognizes handwritten digits, and we will also visualize how the convolutional layers extract important features from the image.

First, let's load the required libraries:

import warnings
warnings.filterwarnings('ignore')

import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.logging.set_verbosity(tf.logging.ERROR)

import matplotlib.pyplot as plt
%matplotlib inline

Load the MNIST dataset:

mnist = input_data.read_data_sets('data/mnist', one_hot=True)

Defining helper functions

Now we define the functions...

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