Jason Brownlee wrote this tutorial to get you started with Deep Learning in TensorFlow. 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.
The article deals with:
- 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 your tf.keras model by reducing overfitting and accelerating training
In this tutorial, you discovered a step-by-step guide to developing deep learning models in TensorFlow using the tf.keras API. Code and links to further resources, including some data sets also included. Well done!
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