2019 | ISBN: n/a | English | 572 pages | PDF | 8,26 MB

Deep learning methods offer a lot of promise for series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

In this new Ebook written in the friendly Machine Learning Mastery style that you're used to, skip the math and jump straight to getting results.
With clear explanations, standard Python libraries (Keras and TensorFlow 2), and step-by-step tutorial lessons you'll discover how to develop deep learning models for your own series forecasting projects.



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