learn_hands_on_ML_v2

notes of reading Aurélien Geron's Hands-on Machine Learning with Scikit-learn, Keras, and TensorFlow (ed2)

• 12. Custom models and training with tensorflow

  • Using TensorFlow like NumPy

    • Tensors and Operations
    • Tensors and NumPy
    • Type Conversions
    • Variables
    • Other Data Structures

  • Customizing Models and Training Algorithms

    • Custom Loss Functions
    • Saving and Loading Models That Contain Custom Components
    • Custom Activation Functions, Initializers, Regularizers, and Constraints
    • Custom Metrics
    • Custom Layers
    • Custom Models
    • Losses and Metrics Based on Model Internals
    • Computing Gradients Using Autodiff
    • Custom Training Loops

  • TensorFlow Functions and Graphs

    • AutoGraph and Tracing
    • TF Function Rules

  • Exercises

• 13. Loading and preprocessing data with TensorFlow

  • The Data API
    • Chaining Transformations
    • Shuffling the Data
    • Preprocessing the Data
    • Putting Everything Together
    • Prefetching
    • Using the Dataset with tf.keras
  • The TFRecord Format
    • Compressed TFRecord Files
    • A Brief Introduction to Protocol Buffers
    • TensorFlow Protobufs
    • Loading and Parsing Examples
    • Handling Lists of Lists Using the SequenceExample Protobuf
  • Preprocessing the Input Features
    • Encoding Categorical Features Using One-Hot Vectors
    • Encoding Categorical Features Using Embeddings
    • Keras Preprocessing Layers
  • TF Transform
  • The TensorFlow Datasets (TFDS) Project
  • Exercises

• 14. Deep Computer Vision Using Convolutional Neural Networks

  • The Architecture of the Visual Cortex
  • Convolutional Layers
    • Filters
    • Stacking Multiple Feature Maps
    • TensorFlow Implementation
    • Memory Requirements
  • Pooling Layers
    • TensorFlow Implementation
  • CNN Architectures
    • LeNet-5
    • AlexNet
    • GoogLeNet
    • VGGNet
    • ResNet
    • Xception
    • SENet
  • Implementing a ResNet-34 CNN Using Keras
  • Using Pretrained Models from Keras
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Object Detection
    • Fully Convolutional Networks
    • You Only Look Once (YOLO)
  • Semantic Segmentation
  • Exercises