Book Image

Deep Learning By Example

Book Image

Deep Learning By Example

Overview of this book

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.
Table of Contents (18 chapters)
16
Implementing Fish Recognition

CNN basic example – MNIST digit classification

In this section, we will do a complete example of implementing a CNN for digit classification using the MNIST dataset. We will build a simple model of two convolution layers and fully connected layers.

Let's start off by importing the libraries that will be needed for this implementation:

%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from sklearn.metrics import confusion_matrix
import math

Next, we will use TensorFlow helper functions to download and preprocess the MNIST dataset as follows:

from tensorflow.examples.tutorials.mnist import input_data
mnist_data = input_data.read_data_sets('data/MNIST/', one_hot=True)
Output:
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting data/MNIST/train-images-idx3-ubyte.gz
Successfully downloaded...