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Python Deep Learning Projects
Python Deep Learning Projects

Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

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Python Deep Learning Projects

Training NN for Prediction Using Regression

Welcome to our first proper project in Python deep learning! What we'll be doing today is building a classifier to solve the problem of identifying specific handwriting samples from a dataset of images. We've been asked (in this hypothetical use case) to do this by a restaurant chain that has the need to accurately classify handwritten numbers into digits. What they have their customers do is write their phone numbers in a simple iPad application. At the time when they can be seated, the guest will get a text prompting them to come and see the restaurant's host. We need to accurately classify the handwritten numbers, so that the output from the app will be accurately predicted labels for the digits of a phone number. This can then be sent to their (hypothetical) auto dialer service for text messages, and the notice gets...

Building a regression model for prediction using an MLP deep neural network

In any real job working in an AI team, one of the primary goals will be to build regression models that can make predictions in non-linear datasets. Because of the complexity of the real world and the data that you'll be working with, simple linear regression models won't provide the predictive power you're seeking. That is why, in this chapter, we will discuss how to build world-class prediction models using MLP. More information can be found at http://www.deeplearningbook.org/contents/mlp.html, and an example of the MLP architecture is shown here:

An MLP with two hidden layers

We will implement a neural network with a simple architecture of only two layers, using TensorFlow, that will perform regression on the MNIST dataset (http://yann.lecun.com/exdb/mnist/) that we will provide. We...

Exploring the MNIST dataset

Before we jump into building our awesome neural network, let's first have a look at the famous MNIST dataset. So let's visualize the MNIST dataset in this section.

Words of wisdom: You must know your data and how it has been preprocessed, in order to know why the models you build perform the way they do. This section reviews the significant work that has been done in preparation on the dataset, to make our current job of building the MLP easier. Always remember: data science begins with DATA!

Let's start therefore by downloading the data, using the following commands:

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

If we examine the mnist variable content, we can see that it is structured in a specific format, with three major components—TRAIN, TEST...

Intuition and preparation

Let's build our intuition around this project. What we need to do is build a deep learning technology that accurately assigns class labels to an input image. We're using a deep neural network, known as an MLP, to do this. The core of this technology is the mathematics of regression. The specific calculus proofs are outside the scope of this book, but in this section, we provide a foundational basis for your understanding. We also outline the structure of the project, so that it's easy to understand the primary steps needed to create our desired results.

Defining regression

Our first task is to define the model that will perform regression on the provided MNIST dataset. So, we will...

Let's code the implementation!

To code the implementation, we'll start by defining the hyperparameters, then we will define the model, followed by building and executing the training loop. We conclude by checking to see if our model is overfitting and build an inference code that loads the latest checkpoints and then makes predictions on the basis of learned parameters.

Defining hyperparameters

We will define all of the required hyperparameters in the hy_param.py file and then import it as a module in our other codes. This makes it easy in deployment, and is good practice to make your code as modular as possible. Let's look into the hyperparameter configurations that we have in our hy_param.py file:

#!/usr/bin...

Concluding the project

Today's project was to build a classifier to solve the problem of identifying specific handwriting samples from a dataset of images. Our hypothetical use case was to apply deep learning to enable customers of a restaurant chain to write their phone numbers in a simple iPad application, so that they could get a text notification that their party was ready to be seated. Our specific task was to build the intelligence that would drive this application.

Revisit our success criteria: How did we do? Did we succeed? What was the impact of our success? Just as we defined success at the beginning of the project, these are the key questions that we need to ask as deep learning data scientists, as we look to wrap up a project.

Our MLP model accuracy hit 87.42%! Not bad, given the depth of the model and the hyperparameters that we chose at the beginning. See if...

Summary

In the project in this chapter, we successfully built an MLP to produce a regression classification prediction, based on handwritten digits. We gained experience with the MNIST dataset and a deep neural network model architecture, which gave us the added opportunity to define some key hyperparameters. Finally, we looked at the model performance in testing and determined whether we succeeded in achieving our goals.

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Key benefits

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural networks and their deployment
  • Access popular deep learning models as well as widely used neural network architectures

Description

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way

Who is this book for?

Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

What you will learn

  • Set up a deep learning development environment on Amazon Web Services (AWS)
  • Apply GPU-powered instances as well as the deep learning AMI
  • Implement seq-to-seq networks for modeling natural language processing (NLP)
  • Develop an end-to-end speech recognition system
  • Build a system for pixel-wise semantic labeling of an image
  • Create a system that generates images and their regions

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Length: 472 pages
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Length: 472 pages
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Language : English
ISBN-13 : 9781788997096
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Table of Contents

16 Chapters
Building Deep Learning Environments Chevron down icon Chevron up icon
Training NN for Prediction Using Regression Chevron down icon Chevron up icon
Word Representation Using word2vec Chevron down icon Chevron up icon
Building an NLP Pipeline for Building Chatbots Chevron down icon Chevron up icon
Sequence-to-Sequence Models for Building Chatbots Chevron down icon Chevron up icon
Generative Language Model for Content Creation Chevron down icon Chevron up icon
Building Speech Recognition with DeepSpeech2 Chevron down icon Chevron up icon
Handwritten Digits Classification Using ConvNets Chevron down icon Chevron up icon
Object Detection Using OpenCV and TensorFlow Chevron down icon Chevron up icon
Building Face Recognition Using FaceNet Chevron down icon Chevron up icon
Automated Image Captioning Chevron down icon Chevron up icon
Pose Estimation on 3D models Using ConvNets Chevron down icon Chevron up icon
Image Translation Using GANs for Style Transfer Chevron down icon Chevron up icon
Develop an Autonomous Agent with Deep R Learning Chevron down icon Chevron up icon
Summary and Next Steps in Your Deep Learning Career Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 25%
4 star 0%
3 star 50%
2 star 0%
1 star 25%
beryl sirmacek Jan 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book for strengthening deep learning skills following the well described examples. You can benefit from this book for your education, teaching activities, as well as for your company and business development in this field.
Amazon Verified review Amazon
bptsj Jan 06, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I plan to spend the next 4mo studying this material and executing the 9 projects. I've had the book for two weeks and so far am not impressed. The first project uses a MLP NN for solving the MNIST handwritten digit problem and concludes the MLP model is not accurate enough (85%). I would have skipped the MLP and just started with the CNN NN. I have done this based on online tutorials and the accuracy was greater than 97%. The book is light on content. On each subject it has a few introductory words then a link to other sources for content and a walk-through on the project code. I think I would have been better off searching the web for the best tutorials on the topics of the 9 projects.To be fair I will update my review when I finish the first 3 projects.
Amazon Verified review Amazon
ABHIJIT Jan 20, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Bad print . Very difficult to read.
Amazon Verified review Amazon
FutureDoc Aug 31, 2023
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
I had such high hopes for this book. It is 5 years old, but I assumed the code on GitHub would have been kept updated, like it implies in the book. But the GitHub files haven't been updated in 5 years. And when I try to run these on my local machine as well as Google Colab, I keep getting version errors. I'm not willing to do all the behind the scenes work to get these to run before I can even use them.
Amazon Verified review Amazon
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