更新时间：2021-06-18 19:08:39
封面
版权信息
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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
Getting Started with Deep Learning
A soft introduction to ML
Working principles of ML algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Putting ML tasks altogether
Delving into deep learning
How did DL take ML into next level?
Artificial Neural Networks
Biological neurons
A brief history of ANNs
How does an ANN learn?
ANNs and the backpropagation algorithm
Forward and backward passes
Weights and biases
Weight optimization
Activation functions
Neural network architectures
Deep neural networks
Multilayer Perceptron
Deep belief networks
Autoencoders
Convolutional neural networks
Recurrent neural networks
Emergent architectures
Residual neural networks
Generative adversarial networks
Capsule networks
DL frameworks and cloud platforms
Deep learning frameworks
Cloud-based platforms for DL
Deep learning from a disaster – Titanic survival prediction
Problem description
Configuring the programming environment
Feature engineering and input dataset preparation
Training MLP classifier
Evaluating the MLP classifier
Frequently asked questions (FAQs)
Summary
Answers to FAQs
Cancer Types Prediction Using Recurrent Type Networks
Deep learning in cancer genomics
Cancer genomics dataset description
Preparing programming environment
Titanic survival revisited with DL4J
Multilayer perceptron network construction
Hidden layer 1
Hidden layer 2
Output layer
Network training
Evaluating the model
Cancer type prediction using an LSTM network
Dataset preparation for training
Recurrent and LSTM networks
Dataset preparation
LSTM network construction
Answers to questions
Multi-Label Image Classification Using Convolutional Neural Networks
Image classification and drawbacks of DNNs
CNN architecture
Convolutional operations
Pooling and padding operations
Fully connected layer (dense layer)
Multi-label image classification using CNNs
Description of the dataset
Removing invalid images
Workflow of the overall project
Image preprocessing
Extracting image metadata
Image feature extraction
Preparing the ND4J dataset
Training evaluating and saving the trained CNN models
Network construction
Scoring the model
Submission file generation
Wrapping everything up by executing the main() method
