Deep learning with PyTorch :: a practical approach to building neural network models using PyTorch /
Build neural network models in text, vision and advanced analytics using PyTorch About This Book Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advan...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Birmingham, UK :
Packt Publishing,
2018.
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| Zusammenfassung: | Build neural network models in text, vision and advanced analytics using PyTorch About This Book Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN's and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries--PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how ... |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788626071 1788626079 1788624335 9781788624336 |
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| 505 | 0 | |a Cover -- Copyright and Credits -- Dedication -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Deep Learning Using PyTorch -- Artificial intelligence -- The history of AI -- Machine learning -- Examples of machine learning in real life -- Deep learning -- Applications of deep learning -- Hype associated with deep learning -- The history of deep learning -- Why now? -- Hardware availability -- Data and algorithms -- Deep learning frameworks -- PyTorch -- Summary -- Chapter 2: Building Blocks of Neural Networks -- Installing PyTorch -- Our first neural network -- Data preparation -- Scalar (0-D tensors) -- Vectors (1-D tensors) -- Matrix (2-D tensors) -- 3-D tensors -- Slicing tensors -- 4-D tensors -- 5-D tensors -- Tensors on GPU -- Variables -- Creating data for our neural network -- Creating learnable parameters -- Neural network model -- Network implementation -- Loss function -- Optimize the neural network -- Loading data -- Dataset class -- DataLoader class -- Summary -- Chapter 3: Diving Deep into Neural Networks -- Deep dive into the building blocks of neural networks -- Layers -- fundamental blocks of neural networks -- Non-linear activations -- Sigmoid -- Tanh -- ReLU -- Leaky ReLU -- PyTorch non-linear activations -- The PyTorch way of building deep learning algorithms -- Model architecture for different machine learning problems -- Loss functions -- Optimizing network architecture -- Image classification using deep learning -- Loading data into PyTorch tensors -- Loading PyTorch tensors as batches -- Building the network architecture -- Training the model -- Summary -- Chapter 4: Fundamentals of Machine Learning -- Three kinds of machine learning problems -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Machine learning glossary. | |
| 505 | 8 | |a Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- Vectorization -- Value normalization -- Handling missing values -- Feature engineering -- Overfitting and underfitting -- Getting more data -- Reducing the size of the network -- Applying weight regularization -- Dropout -- Underfitting -- Workflow of a machine learning project -- Problem definition and dataset creation -- Measure of success -- Evaluation protocol -- Prepare your data -- Baseline model -- Large model enough to overfit -- Applying regularization -- Learning rate picking strategies -- Summary -- Chapter 5: Deep Learning for Computer Vision -- Introduction to neural networks -- MNIST -- getting data -- Building a CNN model from scratch -- Conv2d -- Pooling -- Nonlinear activation -- ReLU -- View -- Linear layer -- Training the model -- Classifying dogs and cats -- CNN from scratch -- Classifying dogs and cats using transfer learning -- Creating and exploring a VGG16 model -- Freezing the layers -- Fine-tuning VGG16 -- Training the VGG16 model -- Calculating pre-convoluted features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors. | |
| 505 | 8 | |a Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model. | |
| 505 | 8 | |a Inception -- Creating an Inception model -- Extracting convolutional features using register_forward_hook -- Creating a new dataset for the convoluted features -- Creating a fully connected model -- Training and validating the model -- Densely connected convolutional networks -- DenseNet -- DenseBlock -- DenseLayer -- Creating a DenseNet model -- Extracting DenseNet features -- Creating a dataset and loaders -- Creating a fully connected model and train -- Model ensembling -- Creating models -- Extracting the image features -- Creating a custom dataset along with data loaders -- Creating an ensembling model -- Training and validating the model -- Encoder-decoder architecture -- Encoder -- Decoder -- Summary -- Chapter 9: What Next? -- What next? -- Overview -- Interesting ideas to explore -- Object detection -- Image segmentation -- OpenNMT in PyTorch -- Alien NLP -- fast.ai -- making neural nets uncool again -- Open Neural Network Exchange -- How to keep yourself updated -- Summary -- Other Books You May Enjoy -- Index. | |
| 520 | |a Build neural network models in text, vision and advanced analytics using PyTorch About This Book Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN's and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries--PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how ... | ||
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| contents | Cover -- Copyright and Credits -- Dedication -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Deep Learning Using PyTorch -- Artificial intelligence -- The history of AI -- Machine learning -- Examples of machine learning in real life -- Deep learning -- Applications of deep learning -- Hype associated with deep learning -- The history of deep learning -- Why now? -- Hardware availability -- Data and algorithms -- Deep learning frameworks -- PyTorch -- Summary -- Chapter 2: Building Blocks of Neural Networks -- Installing PyTorch -- Our first neural network -- Data preparation -- Scalar (0-D tensors) -- Vectors (1-D tensors) -- Matrix (2-D tensors) -- 3-D tensors -- Slicing tensors -- 4-D tensors -- 5-D tensors -- Tensors on GPU -- Variables -- Creating data for our neural network -- Creating learnable parameters -- Neural network model -- Network implementation -- Loss function -- Optimize the neural network -- Loading data -- Dataset class -- DataLoader class -- Summary -- Chapter 3: Diving Deep into Neural Networks -- Deep dive into the building blocks of neural networks -- Layers -- fundamental blocks of neural networks -- Non-linear activations -- Sigmoid -- Tanh -- ReLU -- Leaky ReLU -- PyTorch non-linear activations -- The PyTorch way of building deep learning algorithms -- Model architecture for different machine learning problems -- Loss functions -- Optimizing network architecture -- Image classification using deep learning -- Loading data into PyTorch tensors -- Loading PyTorch tensors as batches -- Building the network architecture -- Training the model -- Summary -- Chapter 4: Fundamentals of Machine Learning -- Three kinds of machine learning problems -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Machine learning glossary. Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- Vectorization -- Value normalization -- Handling missing values -- Feature engineering -- Overfitting and underfitting -- Getting more data -- Reducing the size of the network -- Applying weight regularization -- Dropout -- Underfitting -- Workflow of a machine learning project -- Problem definition and dataset creation -- Measure of success -- Evaluation protocol -- Prepare your data -- Baseline model -- Large model enough to overfit -- Applying regularization -- Learning rate picking strategies -- Summary -- Chapter 5: Deep Learning for Computer Vision -- Introduction to neural networks -- MNIST -- getting data -- Building a CNN model from scratch -- Conv2d -- Pooling -- Nonlinear activation -- ReLU -- View -- Linear layer -- Training the model -- Classifying dogs and cats -- CNN from scratch -- Classifying dogs and cats using transfer learning -- Creating and exploring a VGG16 model -- Freezing the layers -- Fine-tuning VGG16 -- Training the VGG16 model -- Calculating pre-convoluted features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors. Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model. Inception -- Creating an Inception model -- Extracting convolutional features using register_forward_hook -- Creating a new dataset for the convoluted features -- Creating a fully connected model -- Training and validating the model -- Densely connected convolutional networks -- DenseNet -- DenseBlock -- DenseLayer -- Creating a DenseNet model -- Extracting DenseNet features -- Creating a dataset and loaders -- Creating a fully connected model and train -- Model ensembling -- Creating models -- Extracting the image features -- Creating a custom dataset along with data loaders -- Creating an ensembling model -- Training and validating the model -- Encoder-decoder architecture -- Encoder -- Decoder -- Summary -- Chapter 9: What Next? -- What next? -- Overview -- Interesting ideas to explore -- Object detection -- Image segmentation -- OpenNMT in PyTorch -- Alien NLP -- fast.ai -- making neural nets uncool again -- Open Neural Network Exchange -- How to keep yourself updated -- Summary -- Other Books You May Enjoy -- Index. |
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activations -- Sigmoid -- Tanh -- ReLU -- Leaky ReLU -- PyTorch non-linear activations -- The PyTorch way of building deep learning algorithms -- Model architecture for different machine learning problems -- Loss functions -- Optimizing network architecture -- Image classification using deep learning -- Loading data into PyTorch tensors -- Loading PyTorch tensors as batches -- Building the network architecture -- Training the model -- Summary -- Chapter 4: Fundamentals of Machine Learning -- Three kinds of machine learning problems -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Machine learning glossary.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- 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features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Inception -- Creating an Inception model -- Extracting convolutional features using register_forward_hook -- Creating a new dataset for the convoluted features -- Creating a fully connected model -- Training and validating the model -- Densely connected convolutional networks -- DenseNet -- DenseBlock -- DenseLayer -- Creating a DenseNet model -- Extracting DenseNet features -- Creating a dataset and loaders -- Creating a fully connected model and train -- Model ensembling -- Creating models -- Extracting the image features -- Creating a custom dataset along with data loaders -- Creating an ensembling model -- Training and validating the model -- Encoder-decoder architecture -- Encoder -- Decoder -- Summary -- Chapter 9: What Next? -- What next? -- Overview -- Interesting ideas to explore -- Object detection -- Image segmentation -- OpenNMT in PyTorch -- Alien NLP -- fast.ai -- making neural nets uncool again -- Open Neural Network Exchange -- How to keep yourself updated -- Summary -- Other Books You May Enjoy -- Index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Build neural network models in text, vision and advanced analytics using PyTorch About This Book Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN's and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries--PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. 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| id | ZDB-4-EBA-on1028641678 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:28:15Z |
| institution | BVB |
| isbn | 9781788626071 1788626079 1788624335 9781788624336 |
| language | English |
| oclc_num | 1028641678 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (1 volume) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Subramanian, Vishnu, author. Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Vishnu Subramanian. Birmingham, UK : Packt Publishing, 2018. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Online resource; title from title page (Safari, viewed March 13, 2018). Cover -- Copyright and Credits -- Dedication -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Deep Learning Using PyTorch -- Artificial intelligence -- The history of AI -- Machine learning -- Examples of machine learning in real life -- Deep learning -- Applications of deep learning -- Hype associated with deep learning -- The history of deep learning -- Why now? -- Hardware availability -- Data and algorithms -- Deep learning frameworks -- PyTorch -- Summary -- Chapter 2: Building Blocks of Neural Networks -- Installing PyTorch -- Our first neural network -- Data preparation -- Scalar (0-D tensors) -- Vectors (1-D tensors) -- Matrix (2-D tensors) -- 3-D tensors -- Slicing tensors -- 4-D tensors -- 5-D tensors -- Tensors on GPU -- Variables -- Creating data for our neural network -- Creating learnable parameters -- Neural network model -- Network implementation -- Loss function -- Optimize the neural network -- Loading data -- Dataset class -- DataLoader class -- Summary -- Chapter 3: Diving Deep into Neural Networks -- Deep dive into the building blocks of neural networks -- Layers -- fundamental blocks of neural networks -- Non-linear activations -- Sigmoid -- Tanh -- ReLU -- Leaky ReLU -- PyTorch non-linear activations -- The PyTorch way of building deep learning algorithms -- Model architecture for different machine learning problems -- Loss functions -- Optimizing network architecture -- Image classification using deep learning -- Loading data into PyTorch tensors -- Loading PyTorch tensors as batches -- Building the network architecture -- Training the model -- Summary -- Chapter 4: Fundamentals of Machine Learning -- Three kinds of machine learning problems -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Machine learning glossary. Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- Vectorization -- Value normalization -- Handling missing values -- Feature engineering -- Overfitting and underfitting -- Getting more data -- Reducing the size of the network -- Applying weight regularization -- Dropout -- Underfitting -- Workflow of a machine learning project -- Problem definition and dataset creation -- Measure of success -- Evaluation protocol -- Prepare your data -- Baseline model -- Large model enough to overfit -- Applying regularization -- Learning rate picking strategies -- Summary -- Chapter 5: Deep Learning for Computer Vision -- Introduction to neural networks -- MNIST -- getting data -- Building a CNN model from scratch -- Conv2d -- Pooling -- Nonlinear activation -- ReLU -- View -- Linear layer -- Training the model -- Classifying dogs and cats -- CNN from scratch -- Classifying dogs and cats using transfer learning -- Creating and exploring a VGG16 model -- Freezing the layers -- Fine-tuning VGG16 -- Training the VGG16 model -- Calculating pre-convoluted features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors. Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model. Inception -- Creating an Inception model -- Extracting convolutional features using register_forward_hook -- Creating a new dataset for the convoluted features -- Creating a fully connected model -- Training and validating the model -- Densely connected convolutional networks -- DenseNet -- DenseBlock -- DenseLayer -- Creating a DenseNet model -- Extracting DenseNet features -- Creating a dataset and loaders -- Creating a fully connected model and train -- Model ensembling -- Creating models -- Extracting the image features -- Creating a custom dataset along with data loaders -- Creating an ensembling model -- Training and validating the model -- Encoder-decoder architecture -- Encoder -- Decoder -- Summary -- Chapter 9: What Next? -- What next? -- Overview -- Interesting ideas to explore -- Object detection -- Image segmentation -- OpenNMT in PyTorch -- Alien NLP -- fast.ai -- making neural nets uncool again -- Open Neural Network Exchange -- How to keep yourself updated -- Summary -- Other Books You May Enjoy -- Index. Build neural network models in text, vision and advanced analytics using PyTorch About This Book Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn Use PyTorch for GPU-accelerated tensor computations Build custom datasets and data loaders for images and test the models using torchvision and torchtext Build an image classifier by implementing CNN architectures using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a powerful ensemble model Generate new images using GAN's and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries--PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how ... 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 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Data capture & analysis. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Data Processing. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast Python (Computer program language) fast has work: Deep learning with PyTorch (Text) https://id.oclc.org/worldcat/entity/E39PCFxfWFG3CMcXGDwjxVVBRq https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1728031 Volltext |
| spellingShingle | Subramanian, Vishnu Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Cover -- Copyright and Credits -- Dedication -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with Deep Learning Using PyTorch -- Artificial intelligence -- The history of AI -- Machine learning -- Examples of machine learning in real life -- Deep learning -- Applications of deep learning -- Hype associated with deep learning -- The history of deep learning -- Why now? -- Hardware availability -- Data and algorithms -- Deep learning frameworks -- PyTorch -- Summary -- Chapter 2: Building Blocks of Neural Networks -- Installing PyTorch -- Our first neural network -- Data preparation -- Scalar (0-D tensors) -- Vectors (1-D tensors) -- Matrix (2-D tensors) -- 3-D tensors -- Slicing tensors -- 4-D tensors -- 5-D tensors -- Tensors on GPU -- Variables -- Creating data for our neural network -- Creating learnable parameters -- Neural network model -- Network implementation -- Loss function -- Optimize the neural network -- Loading data -- Dataset class -- DataLoader class -- Summary -- Chapter 3: Diving Deep into Neural Networks -- Deep dive into the building blocks of neural networks -- Layers -- fundamental blocks of neural networks -- Non-linear activations -- Sigmoid -- Tanh -- ReLU -- Leaky ReLU -- PyTorch non-linear activations -- The PyTorch way of building deep learning algorithms -- Model architecture for different machine learning problems -- Loss functions -- Optimizing network architecture -- Image classification using deep learning -- Loading data into PyTorch tensors -- Loading PyTorch tensors as batches -- Building the network architecture -- Training the model -- Summary -- Chapter 4: Fundamentals of Machine Learning -- Three kinds of machine learning problems -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Machine learning glossary. Evaluating machine learning models -- Training, validation, and test split -- Simple holdout validation -- K-fold validation -- K-fold validation with shuffling -- Data representativeness -- Time sensitivity -- Data redundancy -- Data preprocessing and feature engineering -- Vectorization -- Value normalization -- Handling missing values -- Feature engineering -- Overfitting and underfitting -- Getting more data -- Reducing the size of the network -- Applying weight regularization -- Dropout -- Underfitting -- Workflow of a machine learning project -- Problem definition and dataset creation -- Measure of success -- Evaluation protocol -- Prepare your data -- Baseline model -- Large model enough to overfit -- Applying regularization -- Learning rate picking strategies -- Summary -- Chapter 5: Deep Learning for Computer Vision -- Introduction to neural networks -- MNIST -- getting data -- Building a CNN model from scratch -- Conv2d -- Pooling -- Nonlinear activation -- ReLU -- View -- Linear layer -- Training the model -- Classifying dogs and cats -- CNN from scratch -- Classifying dogs and cats using transfer learning -- Creating and exploring a VGG16 model -- Freezing the layers -- Fine-tuning VGG16 -- Training the VGG16 model -- Calculating pre-convoluted features -- Understanding what a CNN model learns -- Visualizing outputs from intermediate layers -- Visualizing weights of the CNN layer -- Summary -- Chapter 6: Deep Learning with Sequence Data and Text -- Working with text data -- Tokenization -- Converting text into characters -- Converting text into words -- N-gram representation -- Vectorization -- One-hot encoding -- Word embedding -- Training word embedding by building a sentiment classifier -- Downloading IMDB data and performing text tokenization -- torchtext.data -- torchtext.datasets -- Building vocabulary -- Generate batches of vectors. Creating a network model with embedding -- Training the model -- Using pretrained word embeddings -- Downloading the embeddings -- Loading the embeddings in the model -- Freeze the embedding layer weights -- Recursive neural networks -- Understanding how RNN works with an example -- LSTM -- Long-term dependency -- LSTM networks -- Preparing the data -- Creating batches -- Creating the network -- Training the model -- Convolutional network on sequence data -- Understanding one-dimensional convolution for sequence data -- Creating the network -- Training the model -- Summary -- Chapter 7: Generative Networks -- Neural style transfer -- Loading the data -- Creating the VGG model -- Content loss -- Style loss -- Extracting the losses -- Creating loss function for each layers -- Creating the optimizer -- Training -- Generative adversarial networks -- Deep convolutional GAN -- Defining the generator network -- Transposed convolutions -- Batch normalization -- Generator -- Defining the discriminator network -- Defining loss and optimizer -- Training the discriminator -- Training the discriminator with real images -- Training the discriminator with fake images -- Training the generator network -- Training the complete network -- Inspecting the generated images -- Language modeling -- Preparing the data -- Generating the batches -- Batches -- Backpropagation through time -- Defining a model based on LSTM -- Defining the train and evaluate functions -- Training the model -- Summary -- Chapter 8: Modern Network Architectures -- Modern network architectures -- ResNet -- Creating PyTorch datasets -- Creating loaders for training and validation -- Creating a ResNet model -- Extracting convolutional features -- Creating a custom PyTorch dataset class for the pre-convoluted features and loader -- Creating a simple linear model -- Training and validating the model. Inception -- Creating an Inception model -- Extracting convolutional features using register_forward_hook -- Creating a new dataset for the convoluted features -- Creating a fully connected model -- Training and validating the model -- Densely connected convolutional networks -- DenseNet -- DenseBlock -- DenseLayer -- Creating a DenseNet model -- Extracting DenseNet features -- Creating a dataset and loaders -- Creating a fully connected model and train -- Model ensembling -- Creating models -- Extracting the image features -- Creating a custom dataset along with data loaders -- Creating an ensembling model -- Training and validating the model -- Encoder-decoder architecture -- Encoder -- Decoder -- Summary -- Chapter 9: What Next? -- What next? -- Overview -- Interesting ideas to explore -- Object detection -- Image segmentation -- OpenNMT in PyTorch -- Alien NLP -- fast.ai -- making neural nets uncool again -- Open Neural Network Exchange -- How to keep yourself updated -- 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 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Data capture & analysis. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Data Processing. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast 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/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / |
| title_auth | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / |
| title_exact_search | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / |
| title_full | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Vishnu Subramanian. |
| title_fullStr | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Vishnu Subramanian. |
| title_full_unstemmed | Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Vishnu Subramanian. |
| title_short | Deep learning with PyTorch : |
| title_sort | deep learning with pytorch a practical approach to building neural network models using pytorch |
| title_sub | a practical approach to building neural network models using PyTorch / |
| 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 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. aat Artificial intelligence. bicssc Neural networks & fuzzy systems. bicssc Data capture & analysis. bicssc Computers Intelligence (AI) & Semantics. bisacsh Computers Neural Networks. bisacsh Computers Data Processing. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast 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 Artificial Intelligence Machine Learning Réseaux neuronaux (Informatique) Apprentissage automatique. Python (Langage de programmation) Intelligence artificielle. artificial intelligence. Neural networks & fuzzy systems. Data capture & analysis. Computers Intelligence (AI) & Semantics. Computers Neural Networks. Computers Data Processing. COMPUTERS General. Artificial intelligence Machine learning |
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| work_keys_str_mv | AT subramanianvishnu deeplearningwithpytorchapracticalapproachtobuildingneuralnetworkmodelsusingpytorch |