Deep Learning for Audio with Python

This course introduces the theory and Python implementation of deep learning, including neural networks, backpropagation, gradient descent, CNNs, and RNN-LSTM models. It applies these methods to audio preprocessing and music genre classification using TensorFlow.

Syllabus

1- Deep Learning (for Audio) with Python: Course Overview.
2- AI, machine learning and deep learning.
3- Implementing an artificial neuron from scratch.
4- Vector and matrix operations.
5- Computation in neural networks.
6- Implementing a neural network from scratch in Python.
7- Training a neural network: Backward propagation and gradient descent.
8- TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch.
9- How to implement a (simple) neural network with TensorFlow 2.
10 - Understanding audio data for deep learning.
11- Preprocessing audio data for Deep Learning.
12- Music genre classification: Preparing the dataset.
13- Implementing a neural network for music genre classification.
14- SOLVING OVERFITTING in neural networks.
15- Convolutional Neural Networks Explained Easily.
16- How to Implement a CNN for Music Genre Classification.
17- Recurrent Neural Networks Explained Easily.
18- Long Short Term Memory (LSTM) Networks Explained Easily.
19- How to Implement an RNN-LSTM Network for Music Genre Classification.