Deep learning with Microsoft Cognitive Toolkit quick start guide :: a practical guide to building neural networks using Microsoft's open source deep learning framework /
Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate differ...
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| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Birmingham, UK :
Packt Publishing,
2019.
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| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks. |
| Physical Description: | 1 online resource : illustrations |
| Bibliography: | Includes bibliographical references. |
| ISBN: | 1789803195 9781789803198 |
Staff View
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| 245 | 1 | 0 | |a Deep learning with Microsoft Cognitive Toolkit quick start guide : |b a practical guide to building neural networks using Microsoft's open source deep learning framework / |c Willem Meints. |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2019. | |
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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? | |
| 505 | 8 | |a 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 | |
| 520 | |a Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks. | ||
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| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
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| 776 | 0 | 8 | |i Print version: |a Meints, Willem. |t Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide : A Practical Guide to Building Neural Networks Using Microsoft's Open Source Deep Learning Framework. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789802993 |
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| contents | 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 |
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| publisher | Packt Publishing, |
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| spelling | Meints, Willem, author. Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / Willem Meints. Birmingham, UK : Packt Publishing, 2019. 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references. Online resource; title from title page (Safari, viewed May 8, 2019). 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 Cognitive Toolkit is one of the most popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks. Microsoft Cognitive Toolkit. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Electronic book. has work: Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide (Text) https://id.oclc.org/worldcat/entity/E39PD3383XtkfDbBqQwtQkJ7HC https://id.oclc.org/worldcat/ontology/hasWork Print version: Meints, Willem. Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide : A Practical Guide to Building Neural Networks Using Microsoft's Open Source Deep Learning Framework. Birmingham : Packt Publishing Ltd, ©2019 9781789802993 |
| spellingShingle | Meints, Willem Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / 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 Microsoft Cognitive Toolkit. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / |
| title_auth | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / |
| title_exact_search | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / |
| title_full | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / Willem Meints. |
| title_fullStr | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / Willem Meints. |
| title_full_unstemmed | Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework / Willem Meints. |
| title_short | Deep learning with Microsoft Cognitive Toolkit quick start guide : |
| title_sort | deep learning with microsoft cognitive toolkit quick start guide a practical guide to building neural networks using microsoft s open source deep learning framework |
| title_sub | a practical guide to building neural networks using Microsoft's open source deep learning framework / |
| topic | Microsoft Cognitive Toolkit. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast |
| topic_facet | Microsoft Cognitive Toolkit. Neural networks (Computer science) Machine learning. Artificial intelligence. Neural Networks, Computer Artificial Intelligence Machine Learning Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. Artificial intelligence Machine learning Electronic book. |
| work_keys_str_mv | AT meintswillem deeplearningwithmicrosoftcognitivetoolkitquickstartguideapracticalguidetobuildingneuralnetworksusingmicrosoftsopensourcedeeplearningframework |