Deep Learning (for Audio) with Python: Difference between revisions

From EMC23 - Satellite Of Love
Jump to navigation Jump to search
mNo edit summary
mNo edit summary
Line 34: Line 34:
= Music genre classification: Preparing the dataset =
= Music genre classification: Preparing the dataset =
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="szyGiObZymo" />
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="szyGiObZymo" />
= Implementing a neural network for music genre classification =
<evlplayer id="player1" w="480" h="360" service="youtube" defaultid="_xcFAiufwd0" />


= SOLVING OVERFITTING in neural networks =
= SOLVING OVERFITTING in neural networks =

Revision as of 20:33, 29 August 2021

Deep Learning (for Audio) with Python: Course Overview[edit]

AI, machine learning and deep learning[edit]

Implementing an artificial neuron from scratch[edit]

Vector and matrix operations[edit]

Computation in neural networks[edit]

Implementing a neural network from scratch in Python[edit]

Training a neural network: Backward propagation and gradient descent[edit]

TRAINING A NEURAL NETWORK: Implementing backpropagation and gradient descent from scratch[edit]

How to implement a (simple) neural network with TensorFlow 2[edit]

Understanding audio data for deep learning[edit]

Preprocessing audio data for Deep Learning[edit]

Music genre classification: Preparing the dataset[edit]

Implementing a neural network for music genre classification[edit]

SOLVING OVERFITTING in neural networks[edit]

Convolutional Neural Networks Explained Easily[edit]

How to Implement a CNN for Music Genre Classification[edit]

Recurrent Neural Networks Explained Easily[edit]

Long Short Term Memory (LSTM) Networks Explained Easily[edit]

How to Implement an RNN-LSTM Network for Music Genre Classification[edit]

Retrieved from "https://wiki.emc23.com/?title=Deep_Learning_(for_Audio)_with_Python&oldid=550"