1.1 Basic classification: Use of a small fully connected network in Keras to classify fashion NMIST. tensorflow_tutorials/1-LearnAndUseMachineLearning/1-1-BasicClassification.py
1.2 Text classification with movie reviews: Classifies movie reviews as positive or negative using the text of the review. tensorflow_tutorials/1-LearnAndUseMachineLearning/1-2-TextClassificationWithMovieReviews.py
1.3 House price regression tensorflow_tutorials/1-LearnAndUseMachineLearning/1-3-HousePriceRegression.py
1.4 Explore overfitting and underfitting. tensorflow_tutorials/1-LearnAndUseMachineLearning/1-4-ExploreOverUnderFitting.py
1.5 Saving and Restoring models tensorflow_tutorials/1-LearnAndUseMachineLearning/1-5-SavingAndRestoringModels.py
Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto differentiation. Start with these notebooks, then read the eager execution guide.
2.1 Eager execution tensorflow_tutorials/2-ResearchAndExperimentation_EagerExec/2-1-EagerExecIntro.py
2.2 Automatic differentiation and gradient tape tensorflow_tutorials/2-ResearchAndExperimentation_EagerExec/2-2-AutoDiffGradTape.py
2.3 Custom training: basics tensorflow_tutorials/2-ResearchAndExperimentation_EagerExec/2-3-CustomTrainingBasics.py
2.4 Custom layers tensorflow_tutorials/2-ResearchAndExperimentation_EagerExec/2-4-CustomLayers.py
2.5 Custom training: walkthrough tensorflow_tutorials/2-ResearchAndExperimentation_EagerExec/2-5-CustomTrainWalkthrough.py
3.1. Build a linear model with Estimators
This tutorial uses the tf.estimator API in TensorFlow to solve a benchmark binary classification problem. Estimators are TensorFlow's most scalable and production-oriented model type. tensorflow_tutorials/3-MLatProductionScale/3-1-LinearModel.py
3.2. How to build a simple text classifier with TF-Hub
TF-Hub is a platform to share machine learning expertise packaged in reusable resources, notably pre-trained modules. This tutorial is organized into two main parts.
1. Introduction: Training a text classifier with TF-Hub: We will use a TF-Hub text embedding module to train a simple sentiment classifier with a reasonable baseline accuracy. We will then analyze the predictions to make sure our model is reasonable and propose improvements to increase the accuracy. 2. Advanced: Transfer learning analysis: In this section, we will use various TF-Hub modules to compare their effect on the accuracy of the estimator and demonstrate advantages and pitfalls of transfer learning.
tensorflow_tutorials/3-MLatProductionScale/3-2-TextClassifier.py
3.3 Build a Convolutional Neural Network using Estimators
In this tutorial, you'll learn how to use layers to build a convolutional neural network model to recognize the handwritten digits in the MNIST data set.