Is it for you?

Are you struggling to find an easy to digest and implement material on Deep Learning for Time Series? Then look no further and try the newest book by Jason Brownlee from  Machine Learning Mastery. The book is ‘Deep Learning for Time Series Forecasting‘.

What’s inside?

The book will help you apply classic and deep learning methods for time series forecasting. This book is no exception for what you expect from Machine Learning Mastery books. It is hands-on, practical with plenty of real world examples, and most importantly working and tested code samples that may form the basis for your own experiments.

You may very much like the real application of Deep Learning nets to Household Energy Consumption dataset that was used to train CNN, CNN-LSTM and ConvLSTM networks with good accuracy results.

What’s so special about the book?

I personally was fascinated with the Time Series Classification chapter that applied Deep Learning to Human Activity Recognition (HAR) dataset with quite accurate predictions. What I liked most in HAR is the fact that raw gyros and accelerators measurements from the cell phone were used to train the DL models without any feature engineering. The video of the dataset preparation is shown here.

What’s next?

In the next post I’ll use one of the examples for Human Activity Recognition in the book and try to expand it using Extensions part of the chapter.

If you’ll be able to do it before me, please feel free to provide your feedback in the comments section.

Tip

Did you know that Google’s Colaboratory provides you with the opportunity to use GPUs for free while working on your own Deep Nets implementation? More than that you can easily share these Jupyter notebooks with your peers.