Neural Network Projects with Python电子书

Title Page

Copyright and Credits

Neural Network Projects with Python

Dedication

About Packt

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Packt.com

Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Machine Learning and Neural Networks 101

What is machine learning?

Machine learning algorithms

The machine learning workflow

Setting up your computer for machine learning

Neural networks

Why neural networks?

The basic architecture of neural networks

Training a neural network from scratch in Python

Feedforward

The loss function

Backpropagation

Putting it all together

Deep learning and neural networks

pandas – a powerful data analysis toolkit in Python

pandas DataFrames

Data visualization in pandas

Data preprocessing in pandas

Encoding categorical variables

Imputing missing values

Using pandas in neural network projects

TensorFlow and Keras – open source deep learning libraries

The fundamental building blocks in Keras

Layers – the atom of neural networks in Keras

Models – a collection of layers

Loss function – error metric for neural network training

Optimizers – training algorithm for neural networks

Creating neural networks in Keras

Other Python libraries

Summary

Predicting Diabetes with Multilayer Perceptrons

Technical requirements

Diabetes – understanding the problem

AI in healthcare

Automated diagnosis

The diabetes mellitus dataset

Exploratory data analysis

Data preprocessing

Handling missing values

Data standardization

Splitting the data into training, testing, and validation sets

MLPs

Model architecture

Input layer

Hidden layers

Activation functions

ReLU

Sigmoid activation function

Model building in Python using Keras

Model building

Model compilation

Model training

Results analysis

Testing accuracy

Confusion matrix

ROC curve

Further improvements

Summary

Questions

Predicting Taxi Fares with Deep Feedforward Networks

Technical requirements

Predicting taxi fares in New York City

The NYC taxi fares dataset

Exploratory data analysis

Visualizing geolocation data

Ridership by day and hour

Data preprocessing

Handling missing values and data anomalies

Feature engineering

Temporal features

Geolocation features

Feature scaling

Deep feedforward networks

Model architecture

Loss functions for regression problems

Model building in Python using Keras

Results analysis

Putting it all together

Summary

Questions

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

Filtering and convolution

Max pooling

Basic architecture of CNNs

A review of modern CNNs

LeNet (1998)

AlexNet (2012)

VGG16 (2014)

Inception (2014)

ResNet (2015)

Where we stand today

The cats and dogs dataset

Managing image data for Keras

Image augmentation

Model building

Building a simple CNN

Leveraging on pre-trained models using transfer learning

Results analysis

Summary

Questions

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

Building autoencoders in Keras

Effect of hidden layer size on autoencoder performance

Denoising autoencoders

Deep convolutional denoising autoencoder

Denoising documents with autoencoders

Basic convolutional autoencoder

Deep convolutional autoencoder

Summary

Questions

Sentiment Analysis of Movie Reviews Using LSTM

Technical requirements

Sequential problems in machine learning

NLP and sentiment analysis

Why sentiment analysis is difficult

RNN

What's inside an RNN?

Long- and short-term dependencies in RNNs

The vanishing gradient problem

The LSTM network

LSTMs – the intuition

What's inside an LSTM network?

Forget gate

Input gate

Output gate

Making sense of this

The IMDb movie reviews dataset

Representing words as vectors

One-hot encoding

Word embeddings

Model architecture

Input

Word embedding layer

LSTM layer

Dense layer

Output

Model building in Keras

Importing data

Zero padding

Word embedding and LSTM layers

Compiling and training models

Analyzing the results

Confusion matrix

Putting it all together

Summary

Questions

Implementing a Facial Recognition System with Neural Networks

Technical requirements

Facial recognition systems

Breaking down the face recognition problem

Face detection

Face detection in Python

Face recognition

Requirements of face recognition systems

Speed

Scalability

High accuracy with small data

One-shot learning

Naive one-shot prediction – Euclidean distance between two vectors

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

The onboarding process

Face recognition process

Future work

Summary

Questions

What's Next?

Putting it all together

Machine Learning and Neural Networks 101

Predicting Diabetes with Multilayer Perceptrons

Predicting Taxi Fares with Deep Feedforward Nets

Cats Versus Dogs – Image Classification Using CNNs

Removing Noise from Images Using Autoencoders

Sentiment Analysis of Movie Reviews Using LSTM

Implementing a Facial Recognition System with Neural Networks

Cutting edge advancements in neural networks

Generative adversarial networks

Deep reinforcement learning

Limitations of neural networks

The future of artificial intelligence and machine learning

Artificial general intelligence

Automated machine learning

Keeping up with machine learning

Books

Scientific journals

Practicing on real-world datasets

Favorite machine learning tools

Summary

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