AI and Machine Learning for Coders

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If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics....

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Moroney, Laurence
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol : O'Reilly Media, Incorporated, 2020.
Temas:
Machine learning.
Artificial intelligence.
Apprentissage automatique.
Intelligence artificielle.
artificial intelligence.
Artificial intelligence
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Copyright
  • Table of Contents
  • Foreword
  • Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • Navigating This Book
  • Technology You Need to Understand
  • Online Resources
  • Conventions Used in This Book
  • Using Code Examples
  • O'Reilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • Part I. Building Models
  • Chapter 1. Introduction to TensorFlow
  • What Is Machine Learning?
  • Limitations of Traditional Programming
  • From Programming to Learning
  • What Is TensorFlow?
  • Using TensorFlow
  • Installing TensorFlow in Python
  • Using TensorFlow in PyCharm
  • Using TensorFlow in Google Colab
  • Getting Started with Machine Learning
  • Seeing What the Network Learned
  • Summary
  • Chapter 2. Introduction to Computer Vision
  • Recognizing Clothing Items
  • The Data: Fashion MNIST
  • Neurons for Vision
  • Designing the Neural Network
  • The Complete Code
  • Training the Neural Network
  • Exploring the Model Output
  • Training for Longer-Discovering Overfitting
  • Stopping Training
  • Summary
  • Chapter 3. Going Beyond the Basics: Detecting Features in Images
  • Convolutions
  • Pooling
  • Implementing Convolutional Neural Networks
  • Exploring the Convolutional Network
  • Building a CNN to Distinguish Between Horses and Humans
  • The Horses or Humans Dataset
  • The Keras ImageDataGenerator
  • CNN Architecture for Horses or Humans
  • Adding Validation to the Horses or Humans Dataset
  • Testing Horse or Human Images
  • Image Augmentation
  • Transfer Learning
  • Multiclass Classification
  • Dropout Regularization
  • Summary
  • Chapter 4. Using Public Datasets with TensorFlow Datasets
  • Getting Started with TFDS
  • Using TFDS with Keras Models
  • Loading Specific Versions
  • Using Mapping Functions for Augmentation
  • Using TensorFlow Addons
  • Using Custom Splits
  • Understanding TFRecord
  • The ETL Process for Managing Data in TensorFlow
  • Optimizing the Load Phase
  • Parallelizing ETL to Improve Training Performance
  • Summary
  • Chapter 5. Introduction to Natural Language Processing
  • Encoding Language into Numbers
  • Getting Started with Tokenization
  • Turning Sentences into Sequences
  • Removing Stopwords and Cleaning Text
  • Working with Real Data Sources
  • Getting Text from TensorFlow Datasets
  • Getting Text from CSV Files
  • Getting Text from JSON Files

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