RhythmStudy

오디오 딥러닝 마스터 되기

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Schedule

Date	Plan	Do	Note
22.12.04	Intro	V	계획 생성
22.12.11	PyTorch	V	[PTM] 1장. 딥러닝과 파이토치 라이브러리 소개 [PTM] 2장. 사전 훈련된 신경망 [PTM] 3장. 텐서 구조체
22.12.18	PyTorch	V	[PTM] 4장. 실제 데이터를 텐서로 표현하기 [PTM] 5장. 학습 기법
22.12.28	PyTorch	V	[PTM] 6장. 신경망을 활용한 데이터 적합 [PTM] 7장. 새와 비행기 구별하기: 이미지 학습
23.01.04	PyTorch	V	[PTM] 8장. 컨볼루션을 활용한 일반화 [PTM] 9장. 암과 싸워 이기기 위한 파이토치 활용
23.01.14	PyTorch	V	[PTM] 10장. 여러 데이터 소스를 통합 데이터셋으로 합치기 [PTM] 11장. 종양 탐지를 위한 분류 모델 훈련
23.01.18	PyTorch	V	[BC] Part-1 Basic ML
23.01.25	PyTorch	V	[BC] Part-2 DNN
23.02.01	PyTorch	V	[BC] Part-3 CNN
23.02.08	PyTorch	V	[BC] Part-4 RNN
23.02.19	DSP1	V	1. Overview 2. Sound and waveforms 3.Intensity, loudness, and timbre
23.02.22	DSP2	V	4. Understanding audio signals 5. Types of audio features for ML 6. How to extract audio features
23.03.01	DSP3	V	7. Time-domain audio features 8. Implemneting the amplitude envelope 9. RMS energy and zero-crossing rate
23.03.08	DSP4	V	10. Fourier Transform: The Intuition 11. Complex numbers for audio signal processing 12. Defining the Fourier transform using complex numbers
23.03.18	DSP5	V	13. Discrete Fourier Transform 14. Extracting Spectrograms from Audio with Python 15. Short-Time Fourier Transfom explained easily
23.03.22	DSP6	V	16. Extracting Explained Easily 17. Mel Spectrogram Explained Easily 18. Mel Spectrogram with Python
23.03.29	DSP7	V	19. MFCCs Explained Easily 20. Extracting MFCCs with Python 21. Frequency-Domain Audio Features 22. Implementing Band Energy Ratio from Scratch with Python 23. Spectral centroid and bandwidth
23.04.05	DeepLearningForAudio	V	1. Course overview 2. Ai, machine learning and deep learning 3. Implementing an artificial neuron from scratch 4. Vector and matrix operations
23.04.16	DeepLearningForAudio	V	5. Computation in NN 6. Implementing a NN from scratch 7. Backpropagation and Gradient Descent 8. Training a NN: Implementing back propagation from scratch
23.04.23	DeepLearningForAudio	V	9. How to implement a simple NN with TF 10. Understanding audio data for DL 11. Preprocessing audio data for DL 12. Music genre classification: Prepararing the dataset
23.04.30	DeepLearningForAudio	V	13. Implementing a NN for music genre classification 14. Solving overfitting in NN 15. How does a CNN
23.05.07	DeepLearningForAudio	V	15. How does a CNN 16. How to implement a CNN for music genre classification 17. RNN
23.05.14	DeepLearningForAudio	V	18. LSTM 19. How to implement an RNN-LSTM for music genre classification

Reference

[PyTorch] [파이토치 딥러닝 마스터]

git clone https://github.com/deep-learning-with-pytorch/dlwpt-code

nuyhc / rhythmstudy Goto Github PK

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