Deep Learning - Practical Neural Networks with Java (2017).pdf

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Contents
1: Deep Learning Overview
b'Chapter 1: Deep Learning Overview'
b'Transition of AI'
b'Things dividing a machine and human'
b'AI and deep learning'
b'Summary'
2: Algorithms for Machine Learning
��
Preparing for Deep Learning
b'Chapter 2: Algorithms for Machine Learning \xe2\x80\x93
Preparing for Deep Learning'
b'Getting started'
b'The need for training in machine learning'
b'Supervised and unsupervised learning'
b'Machine learning application flow'
b'Theories and algorithms of neural networks'
b'Summary'
3: Deep Belief Nets and Stacked Denoising Autoencoders
b'Chapter 3: Deep Belief Nets and Stacked Denoising Autoencoders'
b'Neural networks fall'
b'Neural networks' revenge'
b'Deep learning algorithms'
b'Summary'
4: Dropout and Convolutional Neural Networks
b'Chapter 4: Dropout and Convolutional Neural Networks'
b'Deep learning algorithms without pre-training'
b'Dropout'
b'Convolutional neural networks'
b'Summary'
5: Exploring Java Deep Learning Libraries
��
DL4J, ND4J, and More
b'Chapter 5: Exploring Java Deep Learning Libraries \xe2\x80\x93
DL4J, ND4J, and More'
b'Implementing from scratch versus a library/framework'
b'Introducing DL4J and ND4J'
b'Implementations with ND4J'
b'Implementations with DL4J'
b'Summary'
6: Approaches to Practical Applications
��
Recurrent Neural Networks
and More
b'Chapter 6: Approaches to Practical Applications \xe2\x80\x93
Recurrent Neural Networks and More'
b'Fields where deep learning is active'
b'The difficulties of deep learning'
b'The approaches to maximizing deep learning possibilities and
abilities'
b'Summary'
7: Other Important Deep Learning Libraries
b'Chapter 7: Other Important Deep Learning Libraries'
b'Theano'
b'TensorFlow'
b'Caffe'
b'Summary'
8: What's Next?
b'Chapter 8: What's Next?'
b'Breaking news about deep learning'
b'Expected next actions'
b'Useful news sources for deep learning'
b'Summary'
9: Applied Machine Learning Quick Start
b'Chapter 9: Applied Machine Learning Quick Start'
b'Machine learning and data science'
b'Data and problem definition'
b'Data collection'
b'Data pre-processing'
b'Unsupervised learning'
b'Supervised learning'
b'Generalization and evaluation'
b'Summary'
10: Java Libraries and Platforms for Machine Learning
b'Chapter 10: Java Libraries and Platforms for Machine Learning'
b'The need for Java'
b'Machine learning libraries'
b'Building a machine learning application'
b'Summary'
11: Basic Algorithms
��
Classification, Regression, and Clustering
b'Chapter 11: Basic Algorithms \xe2\x80\x93 Classification,
Regression, and Clustering'
b'Before you start'
b'Classification'
b'Regression'
b'Clustering'
b'Summary'
12: Customer Relationship Prediction with Ensembles
b'Chapter 12: Customer Relationship Prediction with Ensembles'
b'Customer relationship database'
b'Basic naive Bayes classifier baseline'
b'Basic modeling'
b'Advanced modeling with ensembles'
b'Summary'
13: Affinity Analysis
b'Chapter 13: Affinity Analysis'
b'Market basket analysis'
b'Association rule learning'
b'The supermarket dataset'
b'Discover patterns'
b'Other applications in various areas'
b'Summary'
14: Recommendation Engine with Apache Mahout
b'Chapter 14: Recommendation Engine with Apache Mahout'
b'Basic concepts'
b'Getting Apache Mahout'
b'Building a recommendation engine'
b'Content-based filtering'
b'Summary'
15: Fraud and Anomaly Detection
b'Chapter 15: Fraud and Anomaly Detection'
b'Suspicious and anomalous behavior detection'
b'Suspicious pattern detection'
b'Anomalous pattern detection'
b'Fraud detection of insurance claims'
b'Anomaly detection in website traffic'
b'Summary'
16: Image Recognition with Deeplearning4j
b'Chapter 16: Image Recognition with Deeplearning4j'
b'Introducing image recognition'
b'Image classification'
b'Summary'
17: Activity Recognition with Mobile Phone Sensors
b'Chapter 17: Activity Recognition with Mobile Phone Sensors'
b'Introducing activity recognition'
b'Collecting data from a mobile phone'
b'Building a classifier'
b'Summary'
18: Text Mining with Mallet
��
Topic Modeling and Spam Detection
b'Chapter 18: Text Mining with Mallet \xe2\x80\x93 Topic Modeling
and Spam Detection'
b'Introducing text mining'
b'Installing Mallet'
b'Working with text data'
b'Topic modeling for BBC news'
b'E-mail spam detection'
b'Summary'
19: What is Next?
b'Chapter 19: What is Next?'
b'Machine learning in real life'
b'Standards and markup languages'
b'Machine learning in the cloud'
b'Web resources and competitions'
b'Summary'
20: Getting Started with Neural Networks
b'Chapter 20: Getting Started with Neural Networks'
b'Discovering neural networks'
b'Why artificial neural networks?'
b'From ignorance to knowledge \xe2\x80\x93 learning process'
b'Let the coding begin! Neural networks in practice'
b'The neuron class'
b'The NeuralLayer class'
b'The ActivationFunction interface'
b'The neural network class'
b'Time to play!'

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