Running deep-learning jobs in the cloud: pros and cons 66
What is the best GPU for deep learning? 66
3.4 Classifying movie reviews: a binary classification
example 68
The IMDB dataset 68
■
Preparing the data 69
Building your network 70
■
Validating your approach 73
Using a trained network to generate predictions on new
data 76
■
Further experiments 77
■
Wrapping up 77
3.5 Classifying newswires: a multiclass classification
example 78
The Reuters dataset 78
■
Preparing the data 79
Building your network 79
■
Validating your approach 80
Generating predictions on new data 83
■
A different way to
handle the labels and the loss 83
■
The importance of
having sufficiently large intermediate layers 83
■
Further
experiments 84
■
Wrapping up 84
3.6 Predicting house prices: a regression example 85
The Boston Housing Price dataset 85
■
Preparing the
data 86
■
Building your network 86
■
Validating
your approach using K-fold validation 87
■
Wrapping up 91
3.7 Chapter summary 92
4
Fundamentals of machine learning 93
4.1 Four branches of machine learning 94
Supervised learning 94
■
Unsupervised learning 94
Self-supervised learning 94
■
Reinforcement learning 95
4.2 Evaluating machine-learning models 97
Training, validation, and test sets 97
■
Things to
keep in mind 100
4.3 Data preprocessing, feature engineering,
and feature learning 101
Data preprocessing for neural networks 101
■
Feature
engineering 102
4.4 Overfitting and underfitting 104
Reducing the network’s size 104
■
Adding weight
regularization 107
■
Adding dropout 109
4.5 The universal workflow of machine learning 111
Defining the problem and assembling a dataset 111
Choosing a measure of success 112
■
Deciding on an
评论0
最新资源