Data Preprocessing and the Short-Time Fourier Transform | Deep Learning for Engineers, Part 3
Data in its raw form might not be ideal for training a network. There are some changes we can make to the data that are often desired or sometimes necessary in order to make training faster, simpler, or to ensure that it converges on a solution in the first place. This video covers three reasons why it’s important for deep learning systems: 1) Preprocessing can transform the data into a form that is suitable for the network architecture 2) Preprocessing can help reduce the dimensions of your data and make patterns more obvious 3) Preprocessing can adjust the training data to ensure the entire solution space is covered. Check out these other resources: • MATLAB Deep learning examples: https://bit.ly/DL-examples • 5 Reasons to use MATLAB for deep learning: https://bit.ly/2QlbNNc • Speech Command Recognition Using Deep Learning: https://bit.ly/2OJCbjo -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2021 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
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