Deep Learning with Pytorch Quick Start Guide :: Learn to Train and Deploy Neural Network Models in Python.
PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. |
| Beschreibung: | 1 online resource (150 pages) |
| ISBN: | 1789539730 9781789539738 |
Internformat
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| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to PyTorch; What is PyTorch?; Installing PyTorch; Digital Ocean; Tunneling in to IPython; Amazon Web Services (AWS); Basic PyTorch operations; Default value initialization; Converting between tensors and NumPy arrays; Slicing and indexing and reshaping; In place operations; Loading data; PyTorch dataset loaders; Displaying an image; DataLoader; Creating a custom dataset; Transforms; ImageFolder; Concatenating datasets; Summary; Chapter 2: Deep Learning Fundamentals | |
| 505 | 8 | |a Approaches to machine learningLearning tasks; Unsupervised learning; Clustering; Principle component analysis; Reinforcement learning; Supervised learning; Classification; Evaluating classifiers; Features; Handling text and categories; Models; Linear algebra review; Linear models; Gradient descent; Multiple features; The normal equation; Logistic regression; Nonlinear models; Artificial neural networks; The perceptron; Summary; Chapter 3: Computational Graphs and Linear Models; autograd; Computational graphs; Linear models; Linear regression in PyTorch; Saving models; Logistic regression | |
| 505 | 8 | |a Activation functions in PyTorchMulti-class classification example; Summary; Chapter 4: Convolutional Networks; Hyper-parameters and multilayered networks; Benchmarking models; Convolutional networks; A single convolutional layer; Multiple kernels; Multiple convolutional layers; Pooling layers; Building a single-layer CNN; Building a multiple-layer CNN; Batch normalization; Summary; Chapter 5: Other NN Architectures; Introduction to recurrent networks; Recurrent artificial neurons ; Implementing a recurrent network; Long short-term memory networks; Implementing an LSTM | |
| 505 | 8 | |a Building a language model with a gated recurrent unitSummary; Chapter 6: Getting the Most out of PyTorch; Multiprocessor and distributed environments; Using a GPU; Distributed environments; torch.distributed; torch.multiprocessing; Optimization techniques; Optimizer algorithms; Learning rate scheduler; Parameter groups; Pretrained models; Implementing a pretrained model; Summary; Other Books You May Enjoy; Index | |
| 520 | |a PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. | ||
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to PyTorch; What is PyTorch?; Installing PyTorch; Digital Ocean; Tunneling in to IPython; Amazon Web Services (AWS); Basic PyTorch operations; Default value initialization; Converting between tensors and NumPy arrays; Slicing and indexing and reshaping; In place operations; Loading data; PyTorch dataset loaders; Displaying an image; DataLoader; Creating a custom dataset; Transforms; ImageFolder; Concatenating datasets; Summary; Chapter 2: Deep Learning Fundamentals Approaches to machine learningLearning tasks; Unsupervised learning; Clustering; Principle component analysis; Reinforcement learning; Supervised learning; Classification; Evaluating classifiers; Features; Handling text and categories; Models; Linear algebra review; Linear models; Gradient descent; Multiple features; The normal equation; Logistic regression; Nonlinear models; Artificial neural networks; The perceptron; Summary; Chapter 3: Computational Graphs and Linear Models; autograd; Computational graphs; Linear models; Linear regression in PyTorch; Saving models; Logistic regression Activation functions in PyTorchMulti-class classification example; Summary; Chapter 4: Convolutional Networks; Hyper-parameters and multilayered networks; Benchmarking models; Convolutional networks; A single convolutional layer; Multiple kernels; Multiple convolutional layers; Pooling layers; Building a single-layer CNN; Building a multiple-layer CNN; Batch normalization; Summary; Chapter 5: Other NN Architectures; Introduction to recurrent networks; Recurrent artificial neurons ; Implementing a recurrent network; Long short-term memory networks; Implementing an LSTM Building a language model with a gated recurrent unitSummary; Chapter 6: Getting the Most out of PyTorch; Multiprocessor and distributed environments; Using a GPU; Distributed environments; torch.distributed; torch.multiprocessing; Optimization techniques; Optimizer algorithms; Learning rate scheduler; Parameter groups; Pretrained models; Implementing a pretrained model; Summary; Other Books You May Enjoy; Index |
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| spelling | Julian, David. Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. Birmingham : Packt Publishing Ltd, 2018. 1 online resource (150 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to PyTorch; What is PyTorch?; Installing PyTorch; Digital Ocean; Tunneling in to IPython; Amazon Web Services (AWS); Basic PyTorch operations; Default value initialization; Converting between tensors and NumPy arrays; Slicing and indexing and reshaping; In place operations; Loading data; PyTorch dataset loaders; Displaying an image; DataLoader; Creating a custom dataset; Transforms; ImageFolder; Concatenating datasets; Summary; Chapter 2: Deep Learning Fundamentals Approaches to machine learningLearning tasks; Unsupervised learning; Clustering; Principle component analysis; Reinforcement learning; Supervised learning; Classification; Evaluating classifiers; Features; Handling text and categories; Models; Linear algebra review; Linear models; Gradient descent; Multiple features; The normal equation; Logistic regression; Nonlinear models; Artificial neural networks; The perceptron; Summary; Chapter 3: Computational Graphs and Linear Models; autograd; Computational graphs; Linear models; Linear regression in PyTorch; Saving models; Logistic regression Activation functions in PyTorchMulti-class classification example; Summary; Chapter 4: Convolutional Networks; Hyper-parameters and multilayered networks; Benchmarking models; Convolutional networks; A single convolutional layer; Multiple kernels; Multiple convolutional layers; Pooling layers; Building a single-layer CNN; Building a multiple-layer CNN; Batch normalization; Summary; Chapter 5: Other NN Architectures; Introduction to recurrent networks; Recurrent artificial neurons ; Implementing a recurrent network; Long short-term memory networks; Implementing an LSTM Building a language model with a gated recurrent unitSummary; Chapter 6: Getting the Most out of PyTorch; Multiprocessor and distributed environments; Using a GPU; Distributed environments; torch.distributed; torch.multiprocessing; Optimization techniques; Optimizer algorithms; Learning rate scheduler; Parameter groups; Pretrained models; Implementing a pretrained model; Summary; Other Books You May Enjoy; Index PyTorch is extremely powerful and yet easy to learn. It provides advanced features such as supporting multiprocessor, distributed and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Neural networks & fuzzy systems. bicssc COMPUTERS General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast Electronic book. Print version: Julian, David. Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. Birmingham : Packt Publishing Ltd, ©2018 9781789534092 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1991439 Volltext |
| spellingShingle | Julian, David Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to PyTorch; What is PyTorch?; Installing PyTorch; Digital Ocean; Tunneling in to IPython; Amazon Web Services (AWS); Basic PyTorch operations; Default value initialization; Converting between tensors and NumPy arrays; Slicing and indexing and reshaping; In place operations; Loading data; PyTorch dataset loaders; Displaying an image; DataLoader; Creating a custom dataset; Transforms; ImageFolder; Concatenating datasets; Summary; Chapter 2: Deep Learning Fundamentals Approaches to machine learningLearning tasks; Unsupervised learning; Clustering; Principle component analysis; Reinforcement learning; Supervised learning; Classification; Evaluating classifiers; Features; Handling text and categories; Models; Linear algebra review; Linear models; Gradient descent; Multiple features; The normal equation; Logistic regression; Nonlinear models; Artificial neural networks; The perceptron; Summary; Chapter 3: Computational Graphs and Linear Models; autograd; Computational graphs; Linear models; Linear regression in PyTorch; Saving models; Logistic regression Activation functions in PyTorchMulti-class classification example; Summary; Chapter 4: Convolutional Networks; Hyper-parameters and multilayered networks; Benchmarking models; Convolutional networks; A single convolutional layer; Multiple kernels; Multiple convolutional layers; Pooling layers; Building a single-layer CNN; Building a multiple-layer CNN; Batch normalization; Summary; Chapter 5: Other NN Architectures; Introduction to recurrent networks; Recurrent artificial neurons ; Implementing a recurrent network; Long short-term memory networks; Implementing an LSTM Building a language model with a gated recurrent unitSummary; Chapter 6: Getting the Most out of PyTorch; Multiprocessor and distributed environments; Using a GPU; Distributed environments; torch.distributed; torch.multiprocessing; Optimization techniques; Optimizer algorithms; Learning rate scheduler; Parameter groups; Pretrained models; Implementing a pretrained model; Summary; Other Books You May Enjoy; Index Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Neural networks & fuzzy systems. bicssc COMPUTERS General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D000069550 https://id.nlm.nih.gov/mesh/D001185 |
| title | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_auth | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_exact_search | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_full | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_fullStr | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_full_unstemmed | Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. |
| title_short | Deep Learning with Pytorch Quick Start Guide : |
| title_sort | deep learning with pytorch quick start guide learn to train and deploy neural network models in python |
| title_sub | Learn to Train and Deploy Neural Network Models in Python. |
| topic | Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Database design & theory. bicssc Data capture & analysis. bicssc Information architecture. bicssc Neural networks & fuzzy systems. bicssc COMPUTERS General. bisacsh Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast |
| topic_facet | Neural networks (Computer science) Machine learning. Python (Computer program language) Artificial intelligence. Neural Networks, Computer Machine Learning Artificial Intelligence Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. Database design & theory. Data capture & analysis. Information architecture. Neural networks & fuzzy systems. COMPUTERS General. Machine learning Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1991439 |
| work_keys_str_mv | AT juliandavid deeplearningwithpytorchquickstartguidelearntotrainanddeployneuralnetworkmodelsinpython |