Deep learning with PyTorch - A practical approach to building neural network models using PyTorch.pdf

Deep learning with PyTorch - A practical approach to building neural network models using PyTorch PDF

Vishnu Subramanian

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as CPUs, 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. Youll 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 Seg2Seq, 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 to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, youll be able to implement deep learning applications in PyTorch with ease.

Work with Deep Learning models and architectures including layers, activations, loss ... Debug your PyTorch code using standard Python tools, so you can easily fix bugs. ... Creating a Neural Network with PyTorch Sequential ... This comprehensive 3-in-1 course takes a practical approach and is filled with real- world ... 2.3 The gears of neural networks: tensor operations 38. Element-wise operations 38 □ ... 7.2 Inspecting and monitoring deep-learning models using ... Introduction to TensorBoard: the TensorFlow visualization framework 252 ... pets and to build practical intuition about the core ideas of machine learning and deep learning.

5.97 MB Taille du fichier
9781788624336 ISBN
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Deep learning with PyTorch - A practical approach to building neural network models using PyTorch.pdf

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Sofya Voigtuh

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch (English Edition), 版本: 1, Packt Publishing, Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch (English Edition) Deep Learning with PyTorch: A practical approach …

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Mattio Müllers

1. Book Cover of Samuel Burns - Python Deep learning: Develop your first Neural ... A practical approach to building neural network models using PyTorch ...

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Noels Schulzen

Neural Networks and Deep Learning Model Zoo. Python 3.7. A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in ...

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Jason Leghmann

Pytorch Introduction | How To Build A Neural Network Building Neural Nets using PyTorch. Let’s understand PyTorch through a more practical lens. Learning theory is good, but it isn’t much use if you don’t put it into practice! A PyTorch implementation of a neural network looks exactly like a NumPy implementation. The goal of this section is to showcase the equivalent nature of PyTorch and

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Jessica Kolhmann

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 to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images.