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Deep Learning with PyTorch
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Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.Perfectly motivating and practical without being shallow.
Table of Contents takes you straight to the bookdetailed table of contents
Part 1: Core PyTorch
1 Introducing Deep Learning and the PyTorch Library
1.1 What is PyTorch?
1.2 What is this book?
1.3 Why PyTorch
1.3.1 The Deep Learning Revolution
1.3.2 Immediate vs. deferred execution
1.3.3 The deep learning competitive landscape
1.4 PyTorch has the batteries included
1.4.1 Hardware for deep learning
1.4.2 Using Jupyter notebooks
1.5 Conclusion
1.6 Exercises
1.7 Summary
2 Pre-Trained Networks
2.1 A pre-trained network that recognizes the subject of an image
2.1.1 Obtaining a pre-trained 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 pre-trained 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 pre-trained 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 Tensors are multi-dimensional arrays
3.1.1 From Python lists to PyTorch tensors
3.1.2 Constructing our first tensors
3.1.3 The essence of tensors
3.2 Indexing Tensors
3.3 Named Tensors
3.4 Tensor element types
3.4.1 Specifying the numeric type with dtype
3.4.2 A dtype for every occasion
3.4.3 Managing a tensor’s dtype attribute
3.5 The tensor API
3.6 Tensors — scenic views on storage
3.6.1 Indexing into storage
3.6.2 Modifying Stored Values — Inplace Operations
3.7 Tensor metadata: size, offset, stride
3.7.1 Views over another tensor’s storage
3.7.2 Transposing without copying
3.7.3 Transposing in higher dimensions
3.7.4 Contiguous tensors
3.8 NumPy interoperability
3.9 Moving tensors to the GPU
3.9.1 Managing a tensor’s device attribute
3.10 Generalized Tensors are Tensors, too
3.11 Serializing tensors
3.11.1 Serializing to HDF5 with h5py
3.12 Conclusion
3.13 Exercises
3.14 Summary
4 Real-World Data Representation Using Tensors
4.1 Images
4.2 Volumetric Data
4.3 Tabular Data
4.4 Time Series
4.5 Text
4.5.1 Text embeddings
4.5.2 Text embeddings a a blueprint
4.6 Conclusion
4.7 Exercises
4.8 Summary
5 The Mechanics of Learning
5.1 Learning is just parameter estimation
5.1.1 A Hot Problem
5.1.2 Choosing a linear model as a first try
5.1.3 Less loss is what we want
5.1.4 From Problem to PyTorch
5.1.5 Down Along the Gradient
5.1.6 Getting Analytical
5.1.7 The Training Loop
5.2 PyTorch’s Autograd: Back-propagate all things
5.2.1 Optimizers a-la Carte
5.2.2 Training, Validation, and Overfitting
5.2.3 Autograd Nits and Switching it Off
5.3 Conclusion
5.4 Exercises
5.5 Summary
6 Using A Neural Network To Fit the Data
6.1 Artificial Neurons
6.1.1 All We Need is Activation
6.1.2 What learning means for a neural network
6.2 The PyTorch nn module
6.2.1 Finally a Neural Network
6.3 Subclassing nn.Module
6.3.1 The Functional API
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 CIFAR10
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 classifier
7.2.3 A loss for classifying
7.2.4 Training the classifier
7.2.5 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
8.2 Convolutions in action
8.2.1 Looking further with depth and pooling
8.3 Subclassing nn.Module
8.3.1 The Functional API
8.4 Training our Convnet
8.5 Model Design
8.5.1 Witdh
8.5.2 Depth
8.5.3 Building very deep models in PyTorch
8.5.4 Now 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 What is a CT scan, exactly?
9.2 The project: an end-to-end malignancy detector for lung cancer
9.2.1 Why can’t we just throw data at a neural network until it works?
9.2.2 What is a nodule?
9.2.3 Our data source: the LUNA Grand Challenge
9.2.4 How to download the LUNA data
9.3 Conclusion
9.4 Summary
10 Ready, Dataset, Go!
10.1 Parsing LUNA’s annotation data
10.2 Loading individual CT scans
10.2.1 Hounsfield Units
10.3 Locating a nodule using the patient coordinate system
10.3.1 Extracting a nodule from a CT scan
10.4 A straightforward Dataset implementation
10.4.1 Caching nodule arrays with the getCtRawNodule function
10.4.2 Constructing our dataset in LunaDataset.{uu}init{uu}
10.4.3 A Training / Validation Split
10.4.4 Rendering the data
10.5 Conclusion
10.6 Exercises
10.7 Summary
11 Training A Classification Model To Detect Suspected Tumors
11.1 The main entrypoint for our application
11.2 Pre-training setup and initialization
11.2.1 Initalizing the model and optimizer
11.2.2 Care and feeding of DataLoaders
11.3 Our first-pass neural network design
11.3.1 The Core Convolutions
11.3.2 The Full Model
11.4 Training and validating the model
11.4.1 Deleting the loss variable
11.4.2 The computeBatchLoss function
11.4.3 The validation loop is similar
11.5 Outputting performance metrics
11.5.1 The logMetrics function
11.6 Running the training script
11.6.1 Needed data for training
11.6.2 Interlude: the enumerateWithEstimate function
11.7 Evaluating the model: Getting 99.7% correct means we’re done, right?
11.8 Graphing training metrics with TensorBoard
11.8.1 Running TensorBoard
11.8.2 Adding tensorboard support to our metrics logging function
11.9 Why is the model not learning to detect malignant tumors?
11.10 Conclusion
11.11 Exercises
11.12 Summary
12 Monitoring Metrics: Precision, Recall, and Pretty Pictures
12.1 Good dogs versus bad guys: false positives and false negatives
12.2 Graphing the positives and negatives
12.2.1 Recall
12.2.2 Precision
12.2.3 Implementing precision and recall in logMetrics
12.2.4 Our ultimate performance metric: the F1 score
12.2.5 How does our model perform with our new metrics?
12.3 What does an ideal data set look like
12.3.1 Making the data look less like the actual and more like the "ideal"
12.3.2 Changes to training.py, dset.py to balance benign and malignant samples
12.3.3 Contrasting training with a balanced LunaDataset to previous runs
12.4 Revisiting the problem of over-fitting
12.4.1 An over-fit face-to-age prediction model
12.4.2 Detecting over-fitting
12.5 Data Augmentation
12.5.1 Specific Data Augmentation Techniques
12.5.2 Seeing the improvement from data augmentation
12.6 Conclusion
12.7 Exercises
12.8 Summary
13 Using Segmentation To Find Suspected Nodules
13.1 Segmentation is per-pixel classification
13.1.1 The UNet architecture
13.1.2 An off-the-shelf model: adding UNet to our project
13.2 A 3D Dataset in 2D
13.2.1 UNet has very specific input size requirements
13.2.2 UNet in 3D would use too much RAM
13.2.3 Building the ground truth data
13.2.4 Implementing the Luna2dSegmentationDataset
13.3 Updating the training script
13.3.1 Getting images into tensorboard
13.3.2 Dice loss
13.3.3 Updating our metrics logging
13.3.4 Saving our model
13.4 Conclusion
13.5 Exercises
13.6 Summary
About the Technology
PyTorch is a machine learning framework with a strong focus on deep neural networks. Because it emphasizes GPU-based acceleration, PyTorch performs exceptionally well on readily-available hardware and scales easily to larger systems. Plus it’s Pythonic! Thanks to its define-by-run computation graph model, PyTorch plays nicely with the Python data science ecosystem. It’s instantly familiar if you’re using Numpy, Pandas, or other similar tools.It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue.
About the book
Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.
What's inside
- Using the PyTorch tensor API
- Understanding automatic differentiation in PyTorch
- Training deep neural networks
- Monitoring training and visualizing results
- Implementing modules and loss functions
- Loading data in Python for PyTorch
- Interoperability with NumPy
- Deploying a PyTorch model for inference
About the reader
Written for developers with some knowledge of Python as well as basic linear algebra skills. Some understanding of deep learning will be helpful, however no experience with PyTorch or other deep learning frameworks is required.About the authors
Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.
Deep Learning with PyTorch authors Luca Antiga (L) and Eli Stevenson (R) eating dessert in San Francisco's Mission District with the book's editor Frances Lefkowitz. Luca is from Bergamo, Italy, Eli lives in San Jose, and Frances hails from San Francisco.
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