1. tensorflow2.0官方教程目录导航
    1. Get started with TensorFlow 2.0
    2. Effective TensorFlow 2.0(高效的tensorflow 2.0)
    3. Migrate from TF 1 to TF 2
    4. Convert with the upgrade script
    5. Get started for beginners (初学者入门 TensorFlow 2.0)
    6. Get started for experts (专家入门TensorFlow 2.0)
  • Beginner tutorials
    1. ML basics
      1. Overview
      2. Classify images (训练您的第一个神经网络:基本分类)
      3. Classify text 使用Keras和TensorFlow Hub对电影评论进行文本分类 (tensorflow2.0官方教程翻译)
      4. Classify structured data (结构化数据分类实战:心脏病预测)
      5. Regression (回归项目实战:预测燃油效率 )
      6. Overfitting and underfitting (探索过拟合和欠拟合)
      7. Save and restore models (tensorflow2保存和加载模型 )
    2. Images
      1. Convolutional Neural Networks (使用TensorFlow2.0实现卷积神经网络CNN对MNIST数字分类)
      2. Transfer learning with TFHub (基于Keras使用TensorFlow Hub实现迁移学习)
      3. Transfer learning with pretrained CNNs (使用预训练的卷积神经网络进行迁移学习)
    3. Text and sequences
      1. Intro to word embeddings (NLP词嵌入Word embedding实战项目)
      2. Classify preprocessed text (文本分类项目实战:电影评论)
      3. Classify text with a RNN (使用RNN对文本进行分类实践:电影评论)
    4. Estimators
      1. Linear models
  • Advanced tutorials
    1. Customization
      1. Overview
      2. Tensors and operations (tensorflow2.0张量及其操作、numpy兼容、GPU加速)
      3. Custom layers (使用Keras自定义层)
      4. Automatic differentiation (TF梯度下降法的核心自动微分和梯度带)
      5. Custom training: basics (构建tensorflow2.0模型自定义训练的基础步骤)
      6. Custom training: walkthrough (使用Keras演示TensorFlow2.0自定义训练实战)
      7. TF function and AutoGraph (TF梯度下降法的核心自动微分和梯度带)
    2. Text and sequences
      1. Generate text with an RNN
      2. Neural Machine Translation with Attention
      3. Image captioning
      4. Transformer model for language understanding
    3. Image Generation
    4. Image Optimization
      1. Style Transfer
    5. GANs
      1. DCGAN
      2. Pix2Pix
    6. Auto Encoders
      1. VAE
    7. Loading data
      1. Load CSV data
      2. Build an image input pipeline
      3. Load text with tf.data
      4. Use TFRecords and tf.Example
      5. Unicode strings
    8. Distributed training
      1. Distributed training
      2. Distributed training with custom training loops
      3. Multi worker training
  • Guide
    1. Eager essentials
    2. Variables
    3. AutoGraph
    4. Keras
      1. Keras overview
      2. Keras functional API
      3. Train and evaluate
      4. Write layers and models from scratch
      5. Save and serialize models
      6. Write custom callbacks
    5. Accelerators
      1. Distribution strategy
      2. Using GPU
    6. Data Loading
      1. Performance
    7. Serialization
      1. Checkpoints
      2. Saved models
    8. Misc
      1. Version Compatibility
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    tensorflow2.0官方教程目录导航

    Get started with TensorFlow 2.0

    Effective TensorFlow 2.0(高效的tensorflow 2.0)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-quickstart-beginner.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/quickstart/beginner

    Migrate from TF 1 to TF 2

    Convert with the upgrade script

    Get started for beginners (初学者入门 TensorFlow 2.0)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-quickstart-beginner.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/quickstart/beginner

    Get started for experts (专家入门TensorFlow 2.0)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-quickstart-advanced.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/quickstart/advanced

    Beginner tutorials

    ML basics

    Overview

    Classify images (训练您的第一个神经网络:基本分类)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-basic_classification.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/basic_classification

    Classify text 使用Keras和TensorFlow Hub对电影评论进行文本分类 (tensorflow2.0官方教程翻译)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-basic_text_classification_with_tfhub.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/basic_text_classification_with_tfhub

    Classify structured data (结构化数据分类实战:心脏病预测)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-feature_columns.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/feature_columns

    Regression (回归项目实战:预测燃油效率 )

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-basic_regression.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/basic_regression

    Overfitting and underfitting (探索过拟合和欠拟合)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-overfit_and_underfit.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/overfit_and_underfit

    Save and restore models (tensorflow2保存和加载模型 )

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-save_and_restore_models.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/save_and_restore_models

    Images

    Convolutional Neural Networks (使用TensorFlow2.0实现卷积神经网络CNN对MNIST数字分类)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-images-intro_to_cnns.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/images/save_and_restore_models

    Transfer learning with TFHub (基于Keras使用TensorFlow Hub实现迁移学习)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-images-hub_with_keras.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/images/hub_with_keras

    Transfer learning with pretrained CNNs (使用预训练的卷积神经网络进行迁移学习)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-images-transfer_learning.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/images/transfer_learning

    Text and sequences

    Intro to word embeddings (NLP词嵌入Word embedding实战项目)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-text-word_embeddings.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/text/word_embeddings

    Classify preprocessed text (文本分类项目实战:电影评论)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-keras-basic_text_classification.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/keras/basic_text_classification

    Classify text with a RNN (使用RNN对文本进行分类实践:电影评论)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-text-text_classification_rnn.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/text/text_classification_rnn

    Estimators

    Linear models

    Advanced tutorials

    Customization

    Overview

    Tensors and operations (tensorflow2.0张量及其操作、numpy兼容、GPU加速)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-basics.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/basics

    Custom layers (使用Keras自定义层)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-custom_layers.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/custom_layers

    Automatic differentiation (TF梯度下降法的核心自动微分和梯度带)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-automatic_differentiation.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/automatic_differentiation

    Custom training: basics (构建tensorflow2.0模型自定义训练的基础步骤)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-custom_training.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/custom_training

    Custom training: walkthrough (使用Keras演示TensorFlow2.0自定义训练实战)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-custom_training_walkthrough.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/custom_training_walkthrough

    TF function and AutoGraph (TF梯度下降法的核心自动微分和梯度带)

    最新版本:https://www.mashangxue123.com/tensorflow/tf2-tutorials-eager-automatic_differentiation.html
    英文版本:https://tensorflow.google.cn/alpha/tutorials/eager/automatic_differentiation

    Text and sequences

    Generate text with an RNN

    Neural Machine Translation with Attention

    Image captioning

    Transformer model for language understanding

    Image Generation

    Image Optimization

    Style Transfer

    GANs

    DCGAN

    Pix2Pix

    Auto Encoders

    VAE

    Loading data

    Load CSV data

    Build an image input pipeline

    Load text with tf.data

    Use TFRecords and tf.Example

    Unicode strings

    Distributed training

    Distributed training

    Distributed training with custom training loops

    Multi worker training

    Guide

    Eager essentials

    Variables

    AutoGraph

    Keras

    Keras overview

    Keras functional API

    Train and evaluate

    Write layers and models from scratch

    Save and serialize models

    Write custom callbacks

    Accelerators

    Distribution strategy

    Using GPU

    Data Loading

    Performance

    Serialization

    Checkpoints

    Saved models

    Misc

    Version Compatibility

    目录