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Deep Learning with PyTorch /

Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including th...

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Detalles Bibliográficos
Autores principales:	Antiga, Luca (Autor), Viehmann, Thomas (Autor), Stevens, Eli (Autor)
Autor Corporativo:	Safari, an O'Reilly Media Company
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Manning Publications, 2020.
Edición:	1st edition.
Temas:	Neural networks (Computer science) Python (Computer program language) Artificial intelligence. Machine learning. Réseaux neuronaux (Informatique) Python (Langage de programmation) Intelligence artificielle. Apprentissage automatique. artificial intelligence. Machine learning Artificial intelligence
Acceso en línea:	Texto completo (Requiere registro previo con correo institucional)

Tabla de Contenidos:

Intro
Copyright
dedication
contents
front matter
foreword
preface
acknowledgments
about this book
Who should read this book
How this book is organized: A roadmap
About the code
Hardware and software requirements
liveBook discussion forum
Other online resources
about the authors
about the cover illustration
Part 1. Core PyTorch
1 Introducing deep learning and the PyTorch Library
1.1 The deep learning revolution
1.2 PyTorch for deep learning
1.3 Why PyTorch?
1.3.1 The deep learning competitive landscape
1.4 An overview of how PyTorch supports deep learning projects
1.5 Hardware and software requirements
1.5.1 Using Jupyter Notebooks
1.6 Exercises
1.7 Summary
2 Pretrained networks
2.1 A pretrained network that recognizes the subject of an image
2.1.1 Obtaining a pretrained network for image recognition
2.1.2 AlexNet
2.1.3 ResNet
2.1.4 Ready, set, almost run
2.1.5 Run!
2.2 A pretrained model that fakes it until it makes it
2.2.1 The GAN game
2.2.2 CycleGAN
2.2.3 A network that turns horses into zebras
2.3 A pretrained network that describes scenes
2.3.1 NeuralTalk2
2.4 Torch Hub
2.5 Conclusion
2.6 Exercises
2.7 Summary
3 It starts with a tensor
3.1 The world as floating-point numbers
3.2 Tensors: Multidimensional arrays
3.2.1 From Python lists to PyTorch tensors
3.2.2 Constructing our first tensors
3.2.3 The essence of tensors
3.3 Indexing tensors
3.4 Named tensors
3.5 Tensor element types
3.5.1 Specifying the numeric type with dtype
3.5.2 A dtype for every occasion
3.5.3 Managing a tensor's dtype attribute
3.6 The tensor API
3.7 Tensors: Scenic views of storage
3.7.1 Indexing into storage
3.7.2 Modifying stored values: In-place operations.
3.8 Tensor metadata: Size, offset, and stride
3.8.1 Views of another tensor's storage
3.8.2 Transposing without copying
3.8.3 Transposing in higher dimensions
3.8.4 Contiguous tensors
3.9 Moving tensors to the GPU
3.9.1 Managing a tensor's device attribute
3.10 NumPy interoperability
3.11 Generalized tensors are tensors, too
3.12 Serializing tensors
3.12.1 Serializing to HDF5 with h5py
3.13 Conclusion
3.14 Exercises
3.15 Summary
4 Real-world data representation using tensors
4.1 Working with images
4.1.1 Adding color channels
4.1.2 Loading an image file
4.1.3 Changing the layout
4.1.4 Normalizing the data
4.2 3D images: Volumetric data
4.2.1 Loading a specialized format
4.3 Representing tabular data
4.3.1 Using a real-world dataset
4.3.2 Loading a wine data tensor
4.3.3 Representing scores
4.3.4 One-hot encoding
4.3.5 When to categorize
4.3.6 Finding thresholds
4.4 Working with time series
4.4.1 Adding a time dimension
4.4.2 Shaping the data by time period
4.4.3 Ready for training
4.5 Representing text
4.5.1 Converting text to numbers
4.5.2 One-hot-encoding characters
4.5.3 One-hot encoding whole words
4.5.4 Text embeddings
4.5.5 Text embeddings as a blueprint
4.6 Conclusion
4.7 Exercises
4.8 Summary
5 The mechanics of learning
5.1 A timeless lesson in modeling
5.2 Learning is just parameter estimation
5.2.1 A hot problem
5.2.2 Gathering some data
5.2.3 Visualizing the data
5.2.4 Choosing a linear model as a first try
5.3 Less loss is what we want
5.3.1 From problem back to PyTorch
5.4 Down along the gradient
5.4.1 Decreasing loss
5.4.2 Getting analytical
5.4.3 Iterating to fit the model
5.4.4 Normalizing inputs
5.4.5 Visualizing (again).
5.5 PyTorch's autograd: Backpropagating all things
5.5.1 Computing the gradient automatically
5.5.2 Optimizers a la carte
5.5.3 Training, validation, and overfitting
5.5.4 Autograd nits and switching it off
5.6 Conclusion
5.7 Exercise
5.8 Summary
6 Using a neural network to fit the data
6.1 Artificial neurons
6.1.1 Composing a multilayer network
6.1.2 Understanding the error function
6.1.3 All we need is activation
6.1.4 More activation functions
6.1.5 Choosing the best activation function
6.1.6 What learning means for a neural network
6.2 The PyTorch nn module
6.2.1 Using __call__ rather than forward
6.2.2 Returning to the linear model
6.3 Finally a neural network
6.3.1 Replacing the linear model
6.3.2 Inspecting the parameters
6.3.3 Comparing to the linear model
6.4 Conclusion
6.5 Exercises
6.6 Summary
7 Telling birds from airplanes: Learning from images
7.1 A dataset of tiny images
7.1.1 Downloading CIFAR-10
7.1.2 The Dataset class
7.1.3 Dataset transforms
7.1.4 Normalizing data
7.2 Distinguishing birds from airplanes
7.2.1 Building the dataset
7.2.2 A fully connected model
7.2.3 Output of a classifier
7.2.4 Representing the output as probabilities
7.2.5 A loss for classifying
7.2.6 Training the classifier
7.2.7 The limits of going fully connected
7.3 Conclusion
7.4 Exercises
7.5 Summary
8 Using convolutions to generalize
8.1 The case for convolutions
8.1.1 What convolutions do
8.2 Convolutions in action
8.2.1 Padding the boundary
8.2.2 Detecting features with convolutions
8.2.3 Looking further with depth and pooling
8.2.4 Putting it all together for our network
8.3 Subclassing nn. Module
8.3.1 Our network as an nn. Module
8.3.2 How PyTorch keeps track of parameters and submodules.
8.3.3 The functional API
8.4 Training our convnet
8.4.1 Measuring accuracy
8.4.2 Saving and loading our model
8.4.3 Training on the GPU
8.5 Model design
8.5.1 Adding memory capacity: Width
8.5.2 Helping our model to converge and generalize: Regularization
8.5.3 Going deeper to learn more complex structures: Depth
8.5.4 Comparing the designs from this section
8.5.5 It's already outdated
8.6 Conclusion
8.7 Exercises
8.8 Summary
Part 2. Learning from images in the real world: Early detection of lung cancer
9 Using PyTorch to fight cancer
9.1 Introduction to the use case
9.2 Preparing for a large-scale project
9.3 What is a CT scan, exactly?
9.4 The project: An end-to-end detector for lung cancer
9.4.1 Why can't we just throw data at a neural network until it works?
9.4.2 What is a nodule?
9.4.3 Our data source: The LUNA Grand Challenge
9.4.4 Downloading the LUNA data
9.5 Conclusion
9.6 Summary
10 Combining data sources into a unified dataset
10.1 Raw CT data files
10.2 Parsing LUNA's annotation data
10.2.1 Training and validation sets
10.2.2 Unifying our annotation and candidate data
10.3 Loading individual CT scans
10.3.1 Hounsfield Units
10.4 Locating a nodule using the patient coordinate system
10.4.1 The patient coordinate system
10.4.2 CT scan shape and voxel sizes
10.4.3 Converting between millimeters and voxel addresses
10.4.4 Extracting a nodule from a CT scan
10.5 A straightforward dataset implementation
10.5.1 Caching candidate arrays with the getCtRawCandidate function
10.5.2 Constructing our dataset in LunaDataset.__init__
10.5.3 A training/validation split
10.5.4 Rendering the data
10.6 Conclusion
10.7 Exercises
Summary
11 Training a classification model to detect suspected tumors.
11.1 A foundational model and training loop
11.2 The main entry point for our application
11.3 Pretraining setup and initialization
11.3.1 Initializing the model and optimizer
11.3.2 Care and feeding of data loaders
11.4 Our first-pass neural network design
11.4.1 The core convolutions
11.4.2 The full model
11.5 Training and validating the model
11.5.1 The computeBatchLoss function
11.5.2 The validation loop is similar
11.6 Outputting performance metrics
11.6.1 The logMetrics function
11.7 Running the training script
11.7.1 Needed data for training
11.7.2 Interlude: The enumerateWithEstimate function
11.8 Evaluating the model: Getting 99.7% correct means we're done, right?
11.9 Graphing training metrics with TensorBoard
11.9.1 Running TensorBoard
11.9.2 Adding TensorBoard support to the metrics logging function
11.10 Why isn't the model learning to detect nodules?
11.11 Conclusion
11.12 Exercises
11.13 Summary
12 Improving training with metrics and augmentation
12.1 High-level plan for improvement
12.2 Good dogs vs. bad guys: False positives and false negatives
12.3 Graphing the positives and negatives
12.3.1 Recall is Roxie's strength
12.3.2 Precision is Preston's forte
12.3.3 Implementing precision and recall in logMetrics
12.3.4 Our ultimate performance metric: The F1 score
12.3.5 How does our model perform with our new metrics?
12.4 What does an ideal dataset look like?
12.4.1 Making the data look less like the actual and more like the "ideal"
12.4.2 Contrasting training with a balanced LunaDataset to previous runs
12.4.3 Recognizing the symptoms of overfitting
12.5 Revisiting the problem of overfitting
12.5.1 An overfit face-to-age prediction model
12.6 Preventing overfitting with data augmentation.

Deep Learning with PyTorch /

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