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Raspberry Pi 3 Cookbook for Python Programmers

Raspberry Pi 3 Cookbook for Python Programmers

By : Steven Lawrence Fernandes, Tim Cox
3.8 (10)
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Raspberry Pi 3 Cookbook for Python Programmers

Raspberry Pi 3 Cookbook for Python Programmers

3.8 (10)
By: Steven Lawrence Fernandes, Tim Cox

Overview of this book

Raspberry Pi 3 Cookbook for Python Programmers – Third Edition begins by guiding you through setting up Raspberry Pi 3, performing tasks using Python 3.6, and introducing the first steps to interface with electronics. As you work through each chapter, you will build your skills and apply them as you progress. You will learn how to build text classifiers, predict sentiments in words, develop applications using the popular Tkinter library, and create games by controlling graphics on your screen. You will harness the power of a built in graphics processor using Pi3D to generate your own high-quality 3D graphics and environments. You will understand how to connect Raspberry Pi’s hardware pins directly to control electronics, from switching on LEDs and responding to push buttons to driving motors and servos. Get to grips with monitoring sensors to gather real-life data, using it to control other devices, and viewing the results over the internet. You will apply what you have learned by creating your own Pi-Rover or Pi-Hexipod robots. You will also learn about sentiment analysis, face recognition techniques, and building neural network modules for optical character recognition. Finally, you will learn to build movie recommendations system on Raspberry Pi 3.
Table of Contents (17 chapters)
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Building an optical character recognizer using neural networks


This section describes the neural network based optical character identification scheme.

How to do it...

  1. Import the following packages:
import numpy as np 
import neurolab as nl 
  1. Read the input file:
in_file = 'words.data'
  1. Consider 20 data points to build the neural network based system:
# Number of datapoints to load from the input file 
num_of_datapoints = 20
  1. Represent the distinct characters:
original_labels = 'omandig' 
# Number of distinct characters 
num_of_charect = len(original_labels) 
  1. Use 90% of data for training the neural network and the remaining 10% for testing:
train_param = int(0.9 * num_of_datapoints) 
test_param = num_of_datapoints - train_param 
  1. Define the dataset extraction parameters:
s_index = 6 
e_index = -1 
  1. Build the dataset:
information = [] 
labels = [] 
with open(in_file, 'r') as f: 
  for line in f.readlines(): 
    # Split the line tabwise 
    list_of_values = line.split('t') 
  1. Implement an error check to confirm...

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