Deep learning for coders with fastai and PyTorch /

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Deep learning has the reputation as an exclusive domain for math PhDs. Not so. With this book, programmers comfortable with Python will learn how to get started with deep learning right away. Using PyTorch and the fastai deep learning library, you'll learn how to train a model to accomplish a w...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Howard, Jeremy (Autor), Gugger, Sylvain (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc., [2020]
Edición:1st edition.
Temas:
Machine learning.
Apprentissage automatique.
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Preface
  • Who This Book Is For
  • What You Need to Know
  • What You Will Learn
  • O'Reilly Online Learning
  • How to Contact Us
  • Foreword
  • I. Deep Learning in Practice
  • 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
  • 2. From Model to Production
  • The Practice of Deep Learning
  • Starting Your Project
  • The State of Deep Learning
  • Computer vision
  • Text (natural language processing)
  • Combining text and images
  • Tabular data
  • Recommendation systems
  • Other data types
  • 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
  • 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?
  • Integrating Machine Learning with Product Design
  • Topics in Data Ethics
  • Recourse and Accountability
  • Feedback Loops
  • Bias
  • Historical bias
  • Measurement bias
  • Aggregation bias
  • Representation bias
  • Addressing different types of bias
  • Disinformation
  • Identifying and Addressing Ethical Issues
  • Analyze a Project You Are Working On
  • Processes to Implement
  • Ethical lenses
  • The Power of Diversity
  • Fairness, Accountability, and Transparency
  • Role of Policy
  • The Effectiveness of Regulation
  • Rights and Policy
  • Cars: A Historical Precedent
  • Conclusion
  • Questionnaire
  • Further Research
  • Deep Learning in Practice: That's a Wrap!
  • II. Understanding fastai's Applications
  • 4. Under the Hood: Training a Digit Classifier
  • Pixels: The Foundations of Computer Vision
  • First Try: Pixel Similarity
  • NumPy Arrays and PyTorch Tensors
  • Computing Metrics Using Broadcasting
  • Stochastic Gradient Descent
  • Calculating Gradients

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