Deep learning with Python: learn best practices of deep learning models with PyTorch
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how w...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Berkeley, CA]
Apress
[2021]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Zusammenfassung: | Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models |
| Beschreibung: | Includes index |
| Beschreibung: | 1 Online-Ressource (xvii, 306 Seiten) illustrations |
| ISBN: | 9781484253649 |
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| 520 | |a Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models | ||
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| author | Ketkar, Nikhil Moolayil, Jojo |
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| contents | Chapter 1 – Introduction Deep Learning -- Chapter 2 – Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 – Automatic Differentiation in Deep Learning -- Chapter 5 – Training Deep Neural Networks -- Chapter 6 – Convolutional Neural Networks -- Chapter 7 – Recurrent Neural Networks -- Chapter 8 – Recent advances in Deep Learning |
| ctrlnum | (OCoLC)1249674843 (DE-599)BVBBV047252371 |
| edition | Second edition |
| format | Electronic eBook |
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| index_date | 2024-07-03T17:08:15Z |
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| institution | BVB |
| isbn | 9781484253649 |
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| spelling | Ketkar, Nikhil Verfasser (DE-588)1136665285 aut Deep learning with Python learn best practices of deep learning models with PyTorch Nikhil Ketkar, Jojo Moolayil Second edition [Berkeley, CA] Apress [2021] 1 Online-Ressource (xvii, 306 Seiten) illustrations txt rdacontent c rdamedia cr rdacarrier Includes index Chapter 1 – Introduction Deep Learning -- Chapter 2 – Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 – Automatic Differentiation in Deep Learning -- Chapter 5 – Training Deep Neural Networks -- Chapter 6 – Convolutional Neural Networks -- Chapter 7 – Recurrent Neural Networks -- Chapter 8 – Recent advances in Deep Learning Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group. You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Machine learning Python (Computer program language) Data mining Moolayil, Jojo Verfasser (DE-588)1176216465 aut Erscheint auch als Druck-Ausgabe 978-1-4842-5363-2 |
| spellingShingle | Ketkar, Nikhil Moolayil, Jojo Deep learning with Python learn best practices of deep learning models with PyTorch Chapter 1 – Introduction Deep Learning -- Chapter 2 – Introduction to PyTorch -- Chapter 3- Feed Forward Networks -- Chapter 4 – Automatic Differentiation in Deep Learning -- Chapter 5 – Training Deep Neural Networks -- Chapter 6 – Convolutional Neural Networks -- Chapter 7 – Recurrent Neural Networks -- Chapter 8 – Recent advances in Deep Learning Machine learning Python (Computer program language) Data mining |
| title | Deep learning with Python learn best practices of deep learning models with PyTorch |
| title_auth | Deep learning with Python learn best practices of deep learning models with PyTorch |
| title_exact_search | Deep learning with Python learn best practices of deep learning models with PyTorch |
| title_exact_search_txtP | Deep learning with Python learn best practices of deep learning models with PyTorch |
| title_full | Deep learning with Python learn best practices of deep learning models with PyTorch Nikhil Ketkar, Jojo Moolayil |
| title_fullStr | Deep learning with Python learn best practices of deep learning models with PyTorch Nikhil Ketkar, Jojo Moolayil |
| title_full_unstemmed | Deep learning with Python learn best practices of deep learning models with PyTorch Nikhil Ketkar, Jojo Moolayil |
| title_short | Deep learning with Python |
| title_sort | deep learning with python learn best practices of deep learning models with pytorch |
| title_sub | learn best practices of deep learning models with PyTorch |
| topic | Machine learning Python (Computer program language) Data mining |
| topic_facet | Machine learning Python (Computer program language) Data mining |
| work_keys_str_mv | AT ketkarnikhil deeplearningwithpythonlearnbestpracticesofdeeplearningmodelswithpytorch AT moolayiljojo deeplearningwithpythonlearnbestpracticesofdeeplearningmodelswithpytorch |