Book Image

Neural Network Projects with Python

By : James Loy
Book Image

Neural Network Projects with Python

By: James Loy

Overview of this book

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.

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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Machine Learning and Neural Networks 101
Machine Learning and Neural Networks 101
What is machine learning?
Setting up your computer for machine learning
Neural networks
pandas – a powerful data analysis toolkit in Python
TensorFlow and Keras – open source deep learning libraries
Other Python libraries
Summary
2
Predicting Diabetes with Multilayer Perceptrons
Predicting Diabetes with Multilayer Perceptrons
Technical requirements
Diabetes – understanding the problem
AI in healthcare
The diabetes mellitus dataset
Exploratory data analysis
Data preprocessing
MLPs
Model building in Python using Keras
Results analysis
Summary
Questions
3
Predicting Taxi Fares with Deep Feedforward Networks
Predicting Taxi Fares with Deep Feedforward Networks
Technical requirements
Predicting taxi fares in New York City
The NYC taxi fares dataset
Exploratory data analysis
Data preprocessing
Feature engineering
Feature scaling
Deep feedforward networks
Model building in Python using Keras
Results analysis
Putting it all together
Summary
Questions
4
Cats Versus Dogs - Image Classification Using CNNs
Cats Versus Dogs - Image Classification Using CNNs
Technical requirements
Computer vision and object recognition
Types of object recognition tasks
Digital images as neural network input
Building blocks of CNNs
Basic architecture of CNNs
A review of modern CNNs
The cats and dogs dataset
Managing image data for Keras
Image augmentation
Model building
Results analysis
Summary
Questions
5
Removing Noise from Images Using Autoencoders
Removing Noise from Images Using Autoencoders
Technical requirements
What are autoencoders?
Latent representation
Autoencoders for data compression
The MNIST handwritten digits dataset
Building a simple autoencoder
Denoising autoencoders
Denoising documents with autoencoders
Summary
Questions
6
Sentiment Analysis of Movie Reviews Using LSTM
Sentiment Analysis of Movie Reviews Using LSTM
Technical requirements
Sequential problems in machine learning
NLP and sentiment analysis
RNN
The LSTM network
The IMDb movie reviews dataset
Representing words as vectors
Model architecture
Model building in Keras
Analyzing the results
Putting it all together
Summary
Questions
7
Implementing a Facial Recognition System with Neural Networks
Implementing a Facial Recognition System with Neural Networks
Technical requirements
Facial recognition systems
Breaking down the face recognition problem
Requirements of face recognition systems
One-shot learning
Siamese neural networks
Contrastive loss
The faces dataset
Creating a Siamese neural network in Keras
Model training in Keras
Analyzing the results
Consolidating our code
Creating a real-time face recognition program
Summary
Questions
8
What's Next?
What's Next?
Putting it all together
Cutting edge advancements in neural networks
Limitations of neural networks
The future of artificial intelligence and machine learning
Keeping up with machine learning
Favorite machine learning tools
Summary
9
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Removing Noise from Images Using Autoencoders

In this chapter, we will study a class of neural networks known as autoencoders, which have gained traction in recent years. In particular, the ability of autoencoders to remove noise from images has been greatly studied. In this chapter, we will build and train an autoencoder that is able to denoise and restore corrupted images.

In this chapter, we'll cover the following topics:

  • What are autoencoders?
  • Unsupervised learning
  • Types of autoencoders—basic autoencoders, deep autoencoders and convolutional autoencoders
  • Autoencoders for image compression
  • Autoencoders for image denoising
  • Step-by-step guide to build and train an autoencoder in Keras
  • Analysis of our results
Previous Section
End of Section 1
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