Explore the theory and implementation of deep learning in Python, focusing on applications for audio and music while covering general algorithms and principles applicable to various domains. Dive into artificial neurons, neural networks, TensorFlow 2, audio data preprocessing, music genre classification, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. Implement neural networks from scratch, tackle overfitting, and gain hands-on experience in applying deep learning techniques to audio-related tasks using TensorFlow.
Overview
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.
Taught by
Valerio Velardo - The Sound of AI
