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001453526 019__ $$a1354273773
001453526 020__ $$a9781484289259$$q(electronic bk.)
001453526 020__ $$a1484289250$$q(electronic bk.)
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001453526 0247_ $$a10.1007/978-1-4842-8925-9$$2doi
001453526 035__ $$aSP(OCoLC)1354205723
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001453526 050_4 $$aQA76.73.P98
001453526 08204 $$a006.3/2$$223/eng/20221213
001453526 1001_ $$aMishra, Pradeepta,$$eauthor.
001453526 24510 $$aPyTorch recipes :$$bA Problem-Solution Approach to Build, Train and Deploy Neural Network Models /$$cPradeepta Mishra.
001453526 250__ $$aSecond edition.
001453526 264_1 $$aBerkeley, CA :$$bApress L. P.,$$c2022.
001453526 300__ $$a1 online resource (xxiv, 266 pages) :$$billustrations
001453526 336__ $$atext$$btxt$$2rdacontent
001453526 337__ $$acomputer$$bc$$2rdamedia
001453526 338__ $$aonline resource$$bcr$$2rdacarrier
001453526 500__ $$aDescription based upon print version of record.
001453526 5058_ $$aIntro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations -- What Is PyTorch? -- PyTorch Installation -- Recipe 1-1. Using Tensors -- Problem -- Solution -- How It Works -- Conclusion -- Chapter 2: Probability Distributions Using PyTorch -- Recipe 2-1. Sampling Tensors -- Problem -- Solution -- How It Works -- Recipe 2-2. Variable Tensors -- Problem -- Solution -- How It Works -- Recipe 2-3. Basic Statistics -- Problem -- Solution -- How It Works
001453526 5058_ $$aRecipe 2-4. Gradient Computation -- Problem -- Solution -- How It Works -- Recipe 2-5. Tensor Operations -- Problem -- Solution -- How It Works -- Recipe 2-6. Tensor Operations -- Problem -- Solution -- How It Works -- Recipe 2-7. Distributions -- Problem -- Solution -- How It Works -- Conclusion -- Chapter 3: CNN and RNN Using PyTorch -- Recipe 3-1. Setting Up a Loss Function -- Problem -- Solution -- How It Works -- Recipe 3-2. Estimating the Derivative of the Loss Function -- Problem -- Solution -- How It Works -- Recipe 3-3. Fine-Tuning a Model -- Problem -- Solution -- How It Works
001453526 5058_ $$aRecipe 3-4. Selecting an Optimization Function -- Problem -- Solution -- How It Works -- Recipe 3-5. Further Optimizing the Function -- Problem -- Solution -- How It Works -- Recipe 3-6. Implementing a Convolutional Neural Network (CNN) -- Problem -- Solution -- How It Works -- Recipe 3-7. Reloading a Model -- Problem -- Solution -- How It Works -- Recipe 3-8. Implementing a Recurrent Neural Network -- Problem -- Solution -- How It Works -- Recipe 3-9. Implementing a RNN for Regression Problems -- Problem -- Solution -- How It Works -- Recipe 3-10. Using PyTorch's Built-In Functions -- Problem
001453526 5058_ $$aSolution -- How It Works -- Recipe 3-11. Working with Autoencoders -- Problem -- Solution -- How It Works -- Recipe 3-12. Fine-Tuning Results Using Autoencoder -- Problem -- Solution -- How It Works -- Recipe 3-13. Restricting Model Overfitting -- Problem -- Solution -- How It Works -- Recipe 3-14. Visualizing the Model Overfit -- Problem -- Solution -- How It Works -- Recipe 3-15. Initializing Weights in the Dropout Rate -- Problem -- Solution -- How It Works -- Recipe 3-16. Adding Math Operations -- Problem -- Solution -- How It Works -- Recipe 3-17. Embedding Layers in RNN -- Problem
001453526 5058_ $$aSolution -- How It Works -- Conclusion -- Chapter 4: Introduction to Neural Networks Using PyTorch -- Recipe 4-1. Working with Activation Functions -- Problem -- Solution -- How It Works -- Linear Function -- Bilinear Function -- Sigmoid Function -- Hyperbolic Tangent Function -- Log Sigmoid Transfer Function -- ReLU Function -- Leaky ReLU -- Recipe 4-2. Visualizing the Shape of Activation Functions -- Problem -- Solution -- How It Works -- Recipe 4-3. Basic Neural Network Model -- Problem -- Solution -- How It Works -- Recipe 4-4. Tensor Differentiation -- Problem -- Solution -- How It Works -- Conclusion
001453526 506__ $$aAccess limited to authorized users.
001453526 520__ $$aLearn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities. By the end of this book, you will be able to confidently build neural network models using PyTorch. What You Will Learn Utilize new code snippets and models to train machine learning models using PyTorch Train deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency to model interpretation Build, train, and deploy neural network models designed to scale with PyTorch Understand best practices for evaluating and fine-tuning models using PyTorch Use advanced torch features in training deep neural networks Explore various neural network models using PyTorch Discover functions compatible with sci-kit learn compatible models Perform distributed PyTorch training and execution Who This Book Is For Machine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.
001453526 650_0 $$aNeural networks (Computer science)
001453526 650_0 $$aMachine learning.
001453526 650_0 $$aPython (Computer program language)
001453526 655_0 $$aElectronic books.
001453526 77608 $$iPrint version:$$aMishra, Pradeepta$$tPyTorch Recipes$$dBerkeley, CA : Apress L. P.,c2022$$z9781484289242
001453526 852__ $$bebk
001453526 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8925-9$$zOnline Access$$91397441.1
001453526 909CO $$ooai:library.usi.edu:1453526$$pGLOBAL_SET
001453526 980__ $$aBIB
001453526 980__ $$aEBOOK
001453526 982__ $$aEbook
001453526 983__ $$aOnline
001453526 994__ $$a92$$bISE