Free online courseDeep Learning for Audio in Python: Neural Networks, CNNs and LSTMs

Course Description

Turn raw sound into reliable predictions by learning how modern deep learning models understand audio. This course takes you from core ideas in AI and machine learning to hands-on deep learning workflows in Python, focusing on the patterns, representations, and model choices that matter when your data is music, speech, or any time-based signal. You will develop an intuition for what neural networks are really computing, why deep learning excels at automatically learning features, and how to think about model capacity, training behavior, and generalization when dealing with complex audio data.

You begin by building key building blocks from scratch: artificial neurons, vector and matrix operations, and the forward and backward passes that make multi-layer networks learn. By implementing backpropagation and gradient descent yourself, you will understand how errors flow through layers, how weights are updated, and why learning can stall or explode when design choices are poor. This foundation makes it much easier to use high-level tooling confidently instead of treating libraries like black boxes.

With the fundamentals in place, you transition to practical modeling with TensorFlow 2 and Keras, learning a clean end-to-end workflow for supervised learning. From there the course shifts into audio-specific deep learning: how to represent sound over time, why time-frequency analysis is essential, and how transforms such as the STFT and feature sets like MFCCs provide compact, learnable inputs for neural networks.

To ground everything in a real application, you work through a complete music genre classification pipeline. You learn how to prepare a dataset in a way that increases the number of training examples and captures time-local musical characteristics, then train models that map learned representations to multi-class outputs. Along the way, you develop the ability to spot and fix overfitting using training and validation behavior, and you gain an understanding of how regularization choices influence results.

Finally, you compare two powerhouse architectures for audio tasks: convolutional neural networks, which excel at learning local patterns in time-frequency maps, and recurrent networks with LSTMs, designed to model longer dependencies across sequences. By understanding how input shapes, data layout, and model assumptions change between CNNs and LSTMs, you will be able to select an approach that matches your problem and iterate with purpose. The result is a practical, job-relevant skill set for building audio AI systems in Python, backed by strong intuition about what your models are learning and why.