1. Wooly Bully - Sam the Sham and the Pharaohs
2. I Can't Help Myself (Sugar Pie Honey Bunch) - Four Tops
3. (I Can't Get No) Satisfaction - The Rolling Stones
4. You Were on My Mind - We Five
5. You've Lost That Lovin' Feelin' - The Righteous Brothers
6. Downtown - Petula Clark
7. Help! - The Beatles
8. Can't You Hear My Heart Beat - Herman's Hermits
9. Crying in the Chapel - Elvis Presley
10. My Girl - The Temptations
11. Help Me, Rhonda - The Beach Boys
12. King of the Road - Roger Miller
13. The Birds and the Bees - Jewel Akens
14. Hold Me, Thrill Me, Kiss Me - Mel Carter
15. Shotgun - Junior Walker & the All Stars
16. I Got You Babe - Sonny & Cher
17. This Diamond Ring - Gary Lewis & the Playboys
18. The 'In' Crowd - Ramsey Lewis Trio
19. Mrs. Brown, You've Got a Lovely Daughter - Herman's Hermits
20. Stop! In the Name of Love - The Supremes
21. Unchained Melody - The Righteous Brothers
22. Silhouettes - Herman's Hermits
23. I'll Never Find Another You - The Seekers
24. Cara Mia - Jay and the Americans
25. Mr. Tambourine Man - The Byrds
26. Cast Your Fate to the Wind - Sounds Orchestral
27. Yes, I'm Ready - Barbara Mason
28. What's New Pussycat? - Tom Jones
29. Eve of Destruction - Barry McGuire
30. Hang On Sloopy - The McCoys

I was inspired to make these videos by this specialization: https://bit.ly/3SqLuA6

In this video we build a simple generative adversarial network based on fully connected layers and train it on the MNIST dataset. It's far from perfect, but it's a start and will lead us to implement more advanced and better architectures in upcoming videos.

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

GAN Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6IjfwIp8

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

OUTLINE:
0:00 - Introduction
0:29 - Building Discriminator
2:14 - Building Generator
4:36 - Hyperparameters, initializations, and preprocessing
10:14 - Setup training of GANs
22:09 - Training and evaluation

A clean, simple and readable from scratch implementation of the EfficientNet architecture (B0-B7) using the PyTorch library.

Original paper:
https://arxiv.org/abs/1905.11946

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

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

❤️ Become a Channel Member:
https://www.youtube.com/channe....l/UCkzW5JSFwvKRjXABI

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH
Ethereum: 0xc84008f43d2E0bC01d925CC35915CdE92c2e99dc

▶️ You Can Connect with me on:
Twitter - https://twitter.com/aladdinpersson
LinkedIn - https://www.linkedin.com/in/al....addin-persson-a95384
Github - https://github.com/aladdinpersson

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

Timestamps:
0:00 - Introduction
0:45 - Imports
1:00 - Architecture config
6:10 - Implementation Structure
7:10 - CNNBlock
10:10 - SqueezeExcitation
13:05 - InvertedResidualBlock (w. Stocasthic depth)
23:44 - EfficientNet
36:22 - Running a small test case
37:55 - Ending