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

Course Description

Learn how to build deep learning systems that understand audio using Python, with a practical path from core concepts to working models. This free online course guides you through the foundations of AI, machine learning, and neural networks, then moves into hands-on implementation so you can confidently translate theory into code.

You will start by creating key building blocks from scratch, including an artificial neuron and a basic neural network, while practicing essential vector and matrix operations used in modern deep learning. You will also explore how computation flows through neural networks and how training works via backpropagation and gradient descent, reinforcing the mechanics behind learning.

After mastering fundamentals, you will transition to building models with TensorFlow 2 and focus on audio as a data type. You will learn how audio is represented, how to preprocess it effectively for deep learning, and how to prepare a dataset for music genre classification.

From there, you will develop and improve audio classification models, including strategies to reduce overfitting. You will also dive into architectures that are especially effective for sound, such as Convolutional Neural Networks for learning patterns in spectrogram-like inputs and Recurrent Neural Networks with LSTMs for sequence-based modeling. By the end, you will be equipped to design, train, and evaluate neural network approaches for real-world audio tasks in Python.