Mastering PyTorch - Second Edition: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond
Jha, Ashish Ranjan
- 出版商: Packt Publishing
- 出版日期: 2024-05-31
- 售價: $2,050
- 貴賓價: 9.5 折 $1,948
- 語言: 英文
- 頁數: 558
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1801074305
- ISBN-13: 9781801074308
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相關分類:
LangChain、DeepLearning
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商品描述
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples
Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks
Purchase of the print or Kindle book includes a free eBook in PDF format
Key Features:- Understand how to use PyTorch to build advanced neural network models
- Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker
- Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks
Book Description:PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most from your data and build complex neural network models.
You'll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You'll deploy PyTorch models to production, including mobile devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face.
By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
What You Will Learn:- Implement text, vision, and music generating models using PyTorch
- Build a deep Q-network (DQN) model in PyTorch
- Deploy PyTorch models on mobile devices (Android and iOS)
- Become well-versed with rapid prototyping using PyTorch with fast.ai
- Perform neural architecture search effectively using AutoML
- Easily interpret machine learning models using Captum
- Design ResNets, LSTMs, and graph neural networks (GNNs)
- Create language and vision transformer models using Hugging Face
Who this book is for:This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
商品描述(中文翻譯)
這本書的標題是「使用PyTorch進階技術和演算法進行機器學習」,它提供了真實世界的例子來幫助讀者學習。這本書已更新至PyTorch 2.x版本,包括與Hugging Face的整合、移動部署、擴散模型和圖形神經網絡等內容。購買印刷版或Kindle電子書的讀者還可以免費獲得PDF格式的電子書。
主要特點包括:
- 瞭解如何使用PyTorch建立高級神經網絡模型
- 通過與Hugging Face、fastai、PyTorch Lightning、PyTorch Geometric、Flask和Docker等工具合作,充分發揮PyTorch的優勢
- 使用多個GPU實現更快的訓練,並使用高效的推理框架優化模型部署
這本書介紹了PyTorch如何使任何人都能夠更輕鬆地構建深度學習應用程序。它將幫助讀者掌握專業技巧,充分利用數據並構建複雜的神經網絡模型。
讀者將學習構建用於圖像分類的卷積神經網絡,以及用於情感分析的循環神經網絡和Transformer模型。隨著進一步的學習,讀者將在不同領域應用深度學習,例如使用生成模型(包括擴散模型)生成音樂、文本和圖像。讀者不僅將在PyTorch中構建和訓練自己的深度強化學習模型,還將學習如何使用多個CPU、GPU和混合精度訓練來優化模型訓練。讀者還將學習將PyTorch模型部署到生產環境中,包括移動設備。最後,讀者將探索PyTorch生態系統及其豐富的庫。這些庫將為深度學習工具箱增添另一套工具,教讀者如何使用fastai進行原型建模,並使用PyTorch Lightning訓練模型。讀者還將了解用於自動機器學習(AutoML)和可解釋人工智能(XAI)的庫,創建推薦系統,並使用Hugging Face構建語言和視覺Transformer模型。
通過閱讀本書,讀者將能夠使用PyTorch執行複雜的深度學習任務,構建智能的人工智能模型。
本書的學習內容包括:
- 使用PyTorch實現文本、視覺和音樂生成模型
- 在PyTorch中構建深度Q網絡(DQN)模型
- 在移動設備(Android和iOS)上部署PyTorch模型
- 熟練使用fast.ai進行快速原型建模
- 使用AutoML有效地進行神經架構搜索
- 使用Captum輕鬆解釋機器學習模型
- 設計ResNet、LSTM和圖形神經網絡(GNN)
- 使用Hugging Face創建語言和視覺Transformer模型
本書適合數據科學家、機器學習工程師、機器學習研究人員和深度學習從業者,他們希望使用PyTorch實現高級深度學習模型。本書也非常適合從TensorFlow轉換到PyTorch的讀者。讀者需要具備使用Python進行深度學習的基礎知識。