Build and train neural network models using TensorFlow 2.x

You need to understand the foundational principles of machine learning (ML) and deep learning (DL) using TensorFlow 2.x. You need to know how to:

  • Use TensorFlow 2.x.
  • Build, compile and train machine learning (ML) models using TensorFlow.
  • Preprocess data to get it ready for use in a model.
  • Use models to predict results.
  • Build sequential models with multiple layers.
  • Build and train models for binary classification.
  • Build and train models for multi-class categorization.
  • Plot loss and accuracy of a trained model.
  • Identify strategies to prevent overfitting, including augmentation and dropout.
  • Use pretrained models (transfer learning)
  • Extract features from pre-trained models.
  • Ensure that that inputs to a model are in the correct shape.
  • Ensure that you can match test data to the input shape of a neural network.
  • Ensure you can match output data of a neural network to specified input shape for test data. 0 U心erstand batch loading of data.
  • Use callbacks to trigger the end of training cydes.
  • Use datasets from different sources.
  • Use datasets in different formats. including json and csv.
  • Use datasets from tf.data.datasets.

Build, compile and train machine learning (ML) models using TensorFlow.

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