http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
https://www.riss.kr/link?id=M16523662
Birmingham, UK : Packt Publishing, 2019
2019
영어
006.32 판사항(23)
1789803195
9781789803198
EBOOK
England
1 online resource : illustrations
Includes bibliographical references.
Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter1: 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 JavaInstalling CNTK; Installing on Windows; Installing Anaconda; Upgrading pip; Installing CNTK; Installing on Linux; Installing Anaconda; 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; Chapter2: 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 functionsModel 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; Summary; Chapter3: Getting Data into Your Neural Network
Technical requirementsTraining 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; Summary; Chapter4: Validating Model Performance; Technical requirements; 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 modelUsing 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
0
상세조회0
다운로드