Deep Learning for Beginners
Dr. Pablo Rivas Laura Montoya更新时间：2021-06-11 18:20:47
最新章节：Leave a review - let other readers know what you think封面
版权信息
About Packt
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Foreword
Contributors
About the author
About the reviewers
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Preface
Who this book is for
What this book covers
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Section 1: Getting Up to Speed
Introduction to Machine Learning
Diving into the ML ecosystem
Training ML algorithms from data
Introducing deep learning
Why is deep learning important today?
Summary
Questions and answers
References
Setup and Introduction to Deep Learning Frameworks
Introduction to Colaboratory
Introduction and setup of TensorFlow
Introduction and setup of Keras
Introduction to PyTorch
Introduction to Dopamine
Other deep learning libraries
Summary
Questions and answers
References
Preparing Data
Binary data and binary classification
Categorical data and multiple classes
Real-valued data and univariate regression
Altering the distribution of data
Data augmentation
Data dimensionality reduction
Ethical implications of manipulating data
Summary
Questions and answers
References
Learning from Data
Learning for a purpose
Measuring success and error
Identifying overfitting and generalization
The art behind learning
Ethical implications of training deep learning algorithms
Summary
Questions and answers
References
Training a Single Neuron
The perceptron model
The perceptron learning algorithm
A perceptron over non-linearly separable data
Summary
Questions and answers
References
Training Multiple Layers of Neurons
The MLP model
Minimizing the error
Finding the best hyperparameters
Summary
Questions and answers
References
Section 2: Unsupervised Deep Learning
Autoencoders
Introduction to unsupervised learning
Encoding and decoding layers
Applications in dimensionality reduction and visualization
Ethical implications of unsupervised learning
Summary
Questions and answers
References
Deep Autoencoders
Introducing deep belief networks
Making deep autoencoders
Exploring latent spaces with deep autoencoders
Summary
Questions and answers
References
Variational Autoencoders
Introducing deep generative models
Examining the VAE model
Comparing a deep and shallow VAE on MNIST
Thinking about the ethical implications of generative models
Summary
Questions and answers
References
Restricted Boltzmann Machines
Introduction to RBMs
Learning data representations with RBMs
Comparing RBMs and AEs
Summary
Questions and answers
References
Section 3: Supervised Deep Learning
Deep and Wide Neural Networks
Wide neural networks
Dense deep neural networks
Sparse deep neural networks
Hyperparameter optimization
Summary
Questions and answers
References
Convolutional Neural Networks
Introduction to convolutional neural networks
Convolution in n-dimensions
Convolutional layers
Pooling strategies
Convolutional neural network for CIFAR-10
Summary
Questions and answers
References
Recurrent Neural Networks
Introduction to recurrent neural networks
Long short-term memory models
Sequence-to-vector models
Vector-to-sequence models
Sequence-to-sequence models
Ethical implications
Summary
Questions and answers
References
Generative Adversarial Networks
Introducing adversarial learning
Training a GAN
Comparing GANs and VAEs
Thinking about the ethical implications of GANs
Summary
Questions and answers
References
Final Remarks on the Future of Deep Learning
Looking for advanced topics in deep learning
Learning with more resources from Packt
Summary
References
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更新时间：2021-06-11 18:20:47