Deep learning for coders with fastai and PyTorch: Al applications without a PhD
Intro -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- What You Need to Know -- What You Will Learn -- O'Reilly Online Learning -- How to Contact Us -- Foreword -- Part I. Deep Learning in Practice -- Chapter 1. Your Deep Learning Journey -- Deep Learning Is for Everyone...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
[2020]
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | UBY01 |
| Zusammenfassung: | Intro -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- What You Need to Know -- What You Will Learn -- O'Reilly Online Learning -- How to Contact Us -- Foreword -- Part I. Deep Learning in Practice -- Chapter 1. Your Deep Learning Journey -- Deep Learning Is for Everyone -- Neural Networks: A Brief History -- Who We Are -- How to Learn Deep Learning -- Your Projects and Your Mindset -- The Software: PyTorch, fastai, and Jupyter (And Why It Doesn't Matter) -- Your First Model -- Getting a GPU Deep Learning Server -- Running Your First Notebook -- What Is Machine Learning? -- What Is a Neural Network? -- A Bit of Deep Learning Jargon -- Limitations Inherent to Machine Learning -- How Our Image Recognizer Works -- What Our Image Recognizer Learned -- Image Recognizers Can Tackle Non-Image Tasks -- Jargon Recap -- Deep Learning Is Not Just for Image Classification -- Validation Sets and Test Sets -- Use Judgment in Defining Test Sets -- A Choose Your Own Adventure Moment -- Questionnaire -- Further Research -- Chapter 2. From Model to Production -- The Practice of Deep Learning -- Starting Your Project -- The State of Deep Learning -- The Drivetrain Approach -- Gathering Data -- From Data to DataLoaders -- Data Augmentation -- Training Your Model, and Using It to Clean Your Data -- Turning Your Model into an Online Application -- Using the Model for Inference -- Creating a Notebook App from the Model -- Turning Your Notebook into a Real App -- Deploying Your App -- How to Avoid Disaster -- Unforeseen Consequences and Feedback Loops -- Get Writing! -- Questionnaire -- Further Research -- Chapter 3. Data Ethics -- Key Examples for Data Ethics -- Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits -- Feedback Loops: YouTube's Recommendation System -- Bias: Professor Latanya Sweeney "Arrested" -- Why Does This Matter?. |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (xxiv, 594 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781492045496 |
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| isbn | 9781492045496 |
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| spelling | Howard, Jeremy Verfasser (DE-588)1220638293 aut Deep learning for coders with fastai and PyTorch Al applications without a PhD Jeremy Howard and Sylvain Gugger First edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly [2020] 1 Online-Ressource (xxiv, 594 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Intro -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- What You Need to Know -- What You Will Learn -- O'Reilly Online Learning -- How to Contact Us -- Foreword -- Part I. Deep Learning in Practice -- Chapter 1. Your Deep Learning Journey -- Deep Learning Is for Everyone -- Neural Networks: A Brief History -- Who We Are -- How to Learn Deep Learning -- Your Projects and Your Mindset -- The Software: PyTorch, fastai, and Jupyter (And Why It Doesn't Matter) -- Your First Model -- Getting a GPU Deep Learning Server -- Running Your First Notebook -- What Is Machine Learning? -- What Is a Neural Network? -- A Bit of Deep Learning Jargon -- Limitations Inherent to Machine Learning -- How Our Image Recognizer Works -- What Our Image Recognizer Learned -- Image Recognizers Can Tackle Non-Image Tasks -- Jargon Recap -- Deep Learning Is Not Just for Image Classification -- Validation Sets and Test Sets -- Use Judgment in Defining Test Sets -- A Choose Your Own Adventure Moment -- Questionnaire -- Further Research -- Chapter 2. From Model to Production -- The Practice of Deep Learning -- Starting Your Project -- The State of Deep Learning -- The Drivetrain Approach -- Gathering Data -- From Data to DataLoaders -- Data Augmentation -- Training Your Model, and Using It to Clean Your Data -- Turning Your Model into an Online Application -- Using the Model for Inference -- Creating a Notebook App from the Model -- Turning Your Notebook into a Real App -- Deploying Your App -- How to Avoid Disaster -- Unforeseen Consequences and Feedback Loops -- Get Writing! -- Questionnaire -- Further Research -- Chapter 3. Data Ethics -- Key Examples for Data Ethics -- Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits -- Feedback Loops: YouTube's Recommendation System -- Bias: Professor Latanya Sweeney "Arrested" -- Why Does This Matter?. Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Deep learning (DE-588)1135597375 s Python Programmiersprache (DE-588)4434275-5 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Gugger, Sylvain Verfasser (DE-588)1220638439 aut Erscheint auch als Druck-Ausgabe Deep learning for coders with fastai and PyTorch First edition Beijing : O'Reilly, 2020 xxiv, 594 Seiten 9781492045526 |
| spellingShingle | Howard, Jeremy Gugger, Sylvain Deep learning for coders with fastai and PyTorch Al applications without a PhD Künstliche Intelligenz (DE-588)4033447-8 gnd Python Programmiersprache (DE-588)4434275-5 gnd Deep learning (DE-588)1135597375 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4434275-5 (DE-588)1135597375 |
| title | Deep learning for coders with fastai and PyTorch Al applications without a PhD |
| title_auth | Deep learning for coders with fastai and PyTorch Al applications without a PhD |
| title_exact_search | Deep learning for coders with fastai and PyTorch Al applications without a PhD |
| title_exact_search_txtP | Deep learning for coders with fastai and PyTorch Al applications without a PhD |
| title_full | Deep learning for coders with fastai and PyTorch Al applications without a PhD Jeremy Howard and Sylvain Gugger |
| title_fullStr | Deep learning for coders with fastai and PyTorch Al applications without a PhD Jeremy Howard and Sylvain Gugger |
| title_full_unstemmed | Deep learning for coders with fastai and PyTorch Al applications without a PhD Jeremy Howard and Sylvain Gugger |
| title_short | Deep learning for coders with fastai and PyTorch |
| title_sort | deep learning for coders with fastai and pytorch al applications without a phd |
| title_sub | Al applications without a PhD |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Python Programmiersprache (DE-588)4434275-5 gnd Deep learning (DE-588)1135597375 gnd |
| topic_facet | Künstliche Intelligenz Python Programmiersprache Deep learning |
| work_keys_str_mv | AT howardjeremy deeplearningforcoderswithfastaiandpytorchalapplicationswithoutaphd AT guggersylvain deeplearningforcoderswithfastaiandpytorchalapplicationswithoutaphd |