Where can I study DSP?

I can recommend online course – Coursera DSP. There are very good introduction in mathematical basis of DSP and review of main DSP themes. Online courses are symbiose of self-study (study time freedom) and regular education (you will have feedback and you can discuss your problems in forum with another students).

How can I learn DSP?

You can learn DSP using a variety of software, including some excellent free programs. There are now texts and other resources aimed at the non-engineer, and it has never been easier to experiment with signal processing–even in real time. So get a book, download some files, and process some signals.

How to be successful with deep learning for signal processing applications?

Being successful with deep learning for signal processing applications depends on your dataset size, your computational power, and how much knowledge you have about the data. You can visualize this in the figure below: The larger and higher quality the dataset, the closer you can get to being able to perform deep learning with raw signal data.

How to bypass deep learning?

To bypass using deep learning, a thorough understanding of signal data and signal processing will be needed to use machine learning techniques that rely on less data than deep learning. #1: Firstly, the process would involve storing, reading, and pre-processing the data.

What are the basic requirements for signal processing?

For signal processing, visualizing is required in the time, frequency, and time-frequency domains for proper exploration. #3: Once the data has been visualized, it will be necessary to transform and extract features from the data such as peaks, change points, and signal patterns.

How to extract features from signals and train deep networks?

You can also use automatic feature extraction techniques, such as Wavelet Scattering, to obtain low-variance features from signals and train deep networks. Deep learning models typically require large amounts of data for training and validation.