In this video we go through how to implement a dynamic algorithm for solving the sequence alignment or edit distance problem. This is also referred to as the Needleman Wunsch algorithm, it seems as if this algorithm has quite many names :)

Code repository:

https://github.com/AladdinPerz....on/Algorithms-Collec

I recommend watching the explanation video before watching this implementation:

https://youtu.be/bQ7kRW6zo9Y

new movie trailer for Fast And Furious 9 in 4K ULTRA HD Quality Starring Vin Diesel

Fast & Furious 9 is an upcoming American action film directed by Justin Lin and written by Daniel Casey. A sequel to 2017's The Fate of the Furious, it will be the ninth installment in the Fast & Furious franchise. The film will star Vin Diesel, Michelle Rodriguez, Jordana Brewster, Tyrese Gibson, Chris "Ludacris" Bridges, Nathalie Emmanuel, John Cena, Helen Mirren, Charlize Theron and Michael Rooker.

Fast & Furious 9 is scheduled to be theatrically released in the United States on May 22, 2020 by Universal Pictures.

New video:
https://youtu.be/IZtv9s_Wx9I

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

Reason for update:
I felt the video explanations could be clearer and better video quality. What I've done now is expand it into a new GAN playlist where DCGAN is one of those! Do check out the links above.

In this video we implement a generative adversarial network (GAN) in Pytorch. Specifically we're implementing a DCGAN (Deep Convolutional Generative Adversarial Network) trained on the MNIST-dataset to generate new digits.

✅ Support My Channel Through Patreon:
https://www.patreon.com/aladdinpersson

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

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

Papers to gain better understanding of GANs:
https://arxiv.org/abs/1406.2661 (Original GAN paper)
https://arxiv.org/abs/1511.06434 (DCGAN paper)
https://arxiv.org/abs/1606.03498 (Techniques for training GANs)

OUTLINE:
0:00 - Introduction
0:46 - Overview of the idea behind GANs
1:42 - Original GAN paper overview
4:27 - DCGAN paper overview
7:13 - Implementation of the Discriminator
12:43 - Implementation of the Generator
17:21 - Initialization of the network, dataset and hyperparameters
24:00 - Setting up the training phase
38:10 - Training the Network and visualizing results