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

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.

學習如何使用PyTorch建立神經網絡模型的程式碼片段，這是第二版更新的內容。本書包含了新的章節，涵蓋了分散式PyTorch建模、在生產環境中部署PyTorch模型以及PyTorch的最新發展，並提供了更新的程式碼。

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LSTM和RNN等深度學習模型。接下來，您將使用PyTorch探索概率分佈概念，以及使用PyTorch進行監督和非監督算法。然後，您將深入研究使用PyTorch構建卷積神經網絡、深度神經網絡和循環神經網絡的模型。本新版還涵蓋了Scorch等主題，這是一個與Scikit機器學習庫相容的模塊，模型量化以減少參數大小，以及為在生產系統中部署模型做準備。詳細介紹了平衡PyTorch工作負載的分散並行處理、使用PyTorch進行圖像處理、音頻分析和模型解釋。每個章節都包含執行特定活動的程式碼片段。

通過閱讀本書，您將能夠自信地使用PyTorch建立神經網絡模型。

您將學到什麼

• 使用新的程式碼片段和模型來使用PyTorch訓練機器學習模型


• 使用更少且更智能的實現來訓練深度學習模型


• 探索PyTorch框架以實現模型的可解釋性和透明度


• 使用PyTorch建立、訓練和部署可擴展的神經網絡模型


• 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高級torch功能


• 使用PyTorch探索各種神經網絡模型


• 發現與sci-kit learn相容模型相容的函數


• 執行分散式PyTorch訓練和執行

本書適合對象機器學習工程師、數據科學家、Python程序員和軟件開發人員，他們有興趣學習PyTorch框架。

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.

Pradeepta Mishra是L&T Infotech（LTI）的AI Fosfor部門主管，領導一個由數據科學家、計算語言學專家、機器學習和深度學習專家組成的大型團隊，致力於打造下一代產品「Leni」，這是世界上第一個虛擬數據科學家。他在人工智能的核心領域具有專業知識，包括自主機器學習和深度學習流程、機器學習運營、圖像處理、音頻處理、自然語言處理（NLP）、自然語言生成（NLG）、專家系統的設計和實施，以及個人數字助手。2019年和2020年，他被《Analytics India》雜誌評為「印度40位40歲以下的頂尖數據科學家」之一。他的兩本書根據廣大需求已經被翻譯成中文和西班牙文。

他在2018年的全球數據科學大會上發表了主題演講。他在官方TEDx YouTube頻道上發表了一場名為「機器能思考嗎？」的TEDx演講。他在全球指導了2000多名數據科學家。他在各個大學、聚會、技術機構和社區組織的論壇上發表了200多場有關數據科學、機器學習、深度學習、自然語言處理和人工智能的技術演講。他是10多所大學的客座教師，教授專業人士深度學習和機器學習，並指導他們在人工智能領域追求有價值的職業生涯。