Pytorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models 2/E
暫譯: PyTorch 食譜：構建、訓練和部署神經網絡模型的問題解決方法（第二版）

Mishra, Pradeepta

Pytorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models 2/E

相關主題

商品描述

Learn 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 ForMachine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.

商品描述(中文翻譯)

學習如何使用 PyTorch 來構建神經網絡模型，並使用本書第二版更新的程式碼片段。本書包含新章節，涵蓋分散式 PyTorch 建模、在生產環境中部署 PyTorch 模型以及有關 PyTorch 的最新發展，並附上更新的程式碼。

您將首先學習如何使用張量來開發和微調神經網絡模型，並實現深度學習模型，如 LSTM 和 RNN。接下來，您將使用 PyTorch 探索機率分佈概念，以及使用 PyTorch 的監督式和非監督式算法。隨後，將深入探討如何使用 PyTorch 構建卷積神經網絡、深度神經網絡和遞迴神經網絡模型。本新版本還涵蓋了 Scorch，這是一個與 Scikit 機器學習庫相容的模組，模型量化以減少參數大小，以及為生產系統中的部署準備模型。還詳細介紹了分散式平行處理以平衡 PyTorch 工作負載、使用 PyTorch 進行影像處理、音頻分析和模型解釋。每章都包含執行特定活動的程式碼片段。

在本書結束時，您將能夠自信地使用 PyTorch 構建神經網絡模型。

您將學到什麼


  • 利用新的程式碼片段和模型來使用 PyTorch 訓練機器學習模型

  • 以更少且更智能的實現訓練深度學習模型

  • 探索 PyTorch 框架以提高模型可解釋性，並為模型解釋帶來透明度

  • 構建、訓練和部署設計為可擴展的神經網絡模型，使用 PyTorch

  • 了解使用 PyTorch 評估和微調模型的最佳實踐

  • 在訓練深度神經網絡時使用進階的 torch 功能

  • 使用 PyTorch 探索各種神經網絡模型

  • 發現與 sci-kit learn 相容的函數

  • 執行分散式 PyTorch 訓練和執行



本書適合誰閱讀機器學習工程師、數據科學家以及對學習 PyTorch 框架感興趣的 Python 程式設計師和軟體開發人員。

作者簡介

Pradeepta Mishra is the Director of AI, Fosfor at L&T Infotech (LTI), leading a large group of Data Scientists, computational linguistics experts, Machine Learning and Deep Learning experts in building the next-generation product, 'Leni, ' the world's first virtual data scientist. He has expertise across core branches of Artificial Intelligence including Autonomous ML and Deep Learning pipelines, ML Ops, Image Processing, Audio Processing, Natural Language Processing (NLP), Natural Language Generation (NLG), design and implementation of expert systems, and personal digital assistants. In 2019 and 2020, he was named one of "India's Top "40Under40DataScientists" by Analytics India Magazine. Two of his books are translated into Chinese and Spanish based on popular demand.

He delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in Artificial Intelligence.

 

 

作者簡介(中文翻譯)

Pradeepta Mishra 是 L&T Infotech (LTI) 的 AI 總監，領導一個大型的數據科學家團隊、計算語言學專家、機器學習和深度學習專家，致力於構建下一代產品「Leni」，這是全球首個虛擬數據科學家。他在人工智慧的核心領域擁有專業知識，包括自主機器學習 (Autonomous ML) 和深度學習管道 (Deep Learning pipelines)、機器學習運營 (ML Ops)、影像處理 (Image Processing)、音訊處理 (Audio Processing)、自然語言處理 (Natural Language Processing, NLP)、自然語言生成 (Natural Language Generation, NLG)、專家系統的設計與實施，以及個人數位助理。他在 2019 年和 2020 年被 Analytics India 雜誌評選為「印度前 40 位 40 歲以下數據科學家」之一。他的兩本書因應廣泛需求已翻譯成中文和西班牙文。

他在 2018 年美國全球數據科學大會上發表了主題演講。他在 TEDx 上發表了題為「機器能思考嗎？」的演講，該演講可在官方 TEDx YouTube 頻道上觀看。他在全球指導了超過 2000 名數據科學家，並在各大學、聚會、技術機構和社區安排的論壇上發表了 200 多場有關數據科學、機器學習 (ML)、深度學習 (DL)、自然語言處理 (NLP) 和人工智慧 (AI) 的技術演講。他是超過 10 所大學的客座教學成員，教授深度學習和機器學習，並指導專業人士追求在人工智慧領域的成功職業生涯。

類似商品