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Oh boy. Hopefully this will leave you with a deep understanding of YOLO and how to implement it from scratch!

Download Dataset here:
https://www.kaggle.com/dataset..../734b7bcb7ef13a045cb

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https://github.com/aladdinpers....son/Machine-Learning

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OUTLINE:
0:00 - Introduction
0:24 - Understanding YOLO
08:25 - Architecture and Implementation
32:00 - Loss Function and Implementation
58:53 - Dataset and Implementation
1:17:50 - Training setup & evaluation
1:40:58 - Thoughts and ending

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In this video, you can get an understanding of the sound generation task, and learn to classify different types of sound generation systems.

I discuss the approaches and Deep Learning models used to generate sound. I also outline the challenges encountered with different methods, and discuss the features used to train generative sound systems.

Slides:
https://github.com/musikalkemi....st/generating-sound-

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===============================

Content:
0:00 Intro
0:33 Defining the sound generation task
1:17 Classification of sound generation systems
2:14 Types of generated sounds
3:41 Sound representations
4:07 Generation from raw audio
7:40 Challenges of raw audio generation
10:21 Generation from spectrograms
16:12 Advantages of generation from spectrograms
18:07 Challenges of generation from spectrograms
20:26 Can we generate sound with MFCCs?
21:26 DL architectures for sound generation
22:13 Inputs for generation
24:03 Details about the sound generative system we'll build
24:44 What's next?
===============================

Mentioned papers:

Wavenet: A Generative Model for Raw Audio:
https://arxiv.org/pdf/1609.03499.pdf

Jukebox: A Generative Model for Music
https://arxiv.org/pdf/2005.00341

DrumGAN: Synthesis of Drum Sounds with Timbral Feature Conditioning Using Generative Adversarial Networks
https://arxiv.org/pdf/2008.12073

Melnet: A generative model for audio in the frequency domain
https://arxiv.org/pdf/1906.01083.pdf

In this video, we are going to implement the GPT2 model from scratch. We are only going to focus on the inference and not on the training logic. We will cover concepts like self attention, decoder blocks and generating new tokens.


Paper: https://openai.com/blog/better-language-models/
Code minGPT: https://github.com/karpathy/minGPT
Code transformers: https://github.com/huggingface..../transformers/blob/0
Code from the video: https://github.com/jankrepl/mi....ldlyoverfitted/tree/

00:00 Intro
01:32 Overview: Main goal [slides]
02:06 Overview: Forward pass [slides]
03:39 Overview: GPT module (part 1) [slides]
04:28 Overview: GPT module (part 2) [slides]
05:25 Overview: Decoder block [slides]
06:10 Overview: Masked self attention [slides]
07:52 Decoder module [code]
13:40 GPT module [code]
18:19 Copying a tensor [code]
19:26 Copying a Decoder module [code]
21:04 Copying a GPT module [code]
22:13 Checking if copying works [code]
26:01 Generating token strategies [demo]
29:10 Generating a token function [code]
32:34 Script (copying + generating) [code]
35:59 Results: Running the script [demo]
40:50 Outro

If you have any video suggestions or you just wanna chat feel free to join the discord server: https://discord.gg/a8Va9tZsG5

Twitter: https://twitter.com/moverfitted

Credits logo animation
Title: Conjungation · Author: Uncle Milk · Source: https://soundcloud.com/unclemilk · License: https://creativecommons.org/licenses/... · Download (9MB): https://auboutdufil.com/?id=600

In this video I walk through the basics of the Hill cipher!

Link to Python implementation: https://www.youtube.com/watch?v=xUEqlzqxSMQ