目录 Foreword Preface Part Ⅰ Building Models 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 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 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 Image Data Generator 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 4.Using Public Datasets with TensorFIow 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 5.Introduction to Natural Language Processing Encoding Language into Numbers Getting Started with T0kenization Turning Sentences into Sequences Removing Stopwords and Cleaning Text Working with Real Data Sources …… Part Ⅱ Using Models Index
以下为对购买帮助不大的评价