更新时间：2021-07-02 12:08:59
coverpage
Title Page
Copyright and Credits
Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Code in Action
Conventions used
Get in touch
Reviews
Getting Started with CNTK
The relationship between AI machine learning and deep learning
Limitations of machine learning
How does deep learning work?
The neural network architecture
Artificial neurons
Predicting output with a neural network
Optimizing a neural network
What is CNTK?
Features of CNTK
A high-speed low-level API
Basic building blocks for quickly creating neural networks
Measuring model performance
Loading and processing large datasets
Using models from C# and Java
Installing CNTK
Installing on Windows
Installing Anaconda
Upgrading pip
Installing on Linux
Upgrading pip to the latest version
Installing the CNTK package
Using your GPU with CNTK
Enabling GPU usage on Windows
Enabling GPU usage on Linux
Summary
Building Neural Networks with CNTK
Technical requirements
Basic neural network concepts in CNTK
Building neural networks using layer functions
Customizing layer settings
Using learners and trainers to optimize the parameters in a neural network
Loss functions
Model metrics
Building your first neural network
Building the network structure
Choosing an activation function
Choosing an activation function for the output layer
Choosing an activation function for the hidden layers
Picking a loss function
Recording metrics
Training the neural network
Choosing a learner and setting up training
Feeding data into the trainer to optimize the neural network
Checking the performance of the neural network
Making predictions with a neural network
Improving the model
Getting Data into Your Neural Network
Training a neural network efficiently with minibatches
Working with small in-memory datasets
Working with numpy arrays
Working with pandas DataFrames
Working with large datasets
Creating a MinibatchSource instance
Creating CTF files
Feeding data into a training session
Taking control over the minibatch loop
Validating Model Performance
Choosing a good strategy to validate model performance
Using a hold-out dataset for validation
Using k-fold cross-validation
What about underfitting and overfitting?
Validating performance of a classification model
Using a confusion matrix to validate your classification model
Using the F-measure as an alternative to the confusion matrix
Measuring classification performance in CNTK
Validating performance of a regression model
Measuring the accuracy of your predictions
Measuring regression model performance in CNTK
Measuring performance for out-of-memory datasets
Measuring performance when working with minibatch sources
Measuring performance when working with a manual minibatch loop
Monitoring your model
