Machine Learning

TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras

Last Updated on August 27, 2020

Predictive modeling with deep learning is a skill that modern developers need to know.

TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API beings the simplicity and ease of use of Keras to the TensorFlow project.

Using tf.keras allows you to design, fit, evaluate, and use deep learning models to make predictions in just a few lines of code. It makes common deep learning tasks, such as classification and regression predictive modeling, accessible to average developers looking to get things done.

In this tutorial, you will discover a step-by-step guide to developing deep learning models in TensorFlow using the tf.keras API.

After completing this tutorial, you will know:

  • The difference between Keras and tf.keras and how to install and confirm TensorFlow is working.
  • The 5-step life-cycle of tf.keras models and how to use the sequential and functional APIs.
  • How to develop MLP, CNN, and RNN models with tf.keras for regression, classification, and time series forecasting.
  • How to use the advanced features of the tf.keras API to inspect and diagnose your model.
  • How to improve the performance of
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