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

Image-to-Image Translation with Conditional Adversarial Networks: https://arxiv.org/abs/1611.07004

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

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#PaperReview #PaperExplained

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

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

Timestamps:
0:00 - Introduction
1:29 - Overview of paper
2:25 - Why GANs for Pix2Pix
3:16 - Loss Function
5:12 - Generator Architecture
9:24 - Discriminator Architecture
12:00 - Some training details
13:24 - Turkers to evaluate GANs
14:10 - Patch size for Discriminator
15:19 - Generator works for larger images
15:50 - More details for implementation
19:05 - Ending

This Complete Machine Learning Course video will help you understand and learn Machine Learning in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning concepts & practice.

All presentation files for the Machine Learning course as PDF for as low as ₹200 (INR): https://forms.gle/VeD1hi5e2n6Ced236

Github repository link for Colab Notebooks: https://github.com/siddhardhan....23/Complete-Machine-

All Datasets Link: https://drive.google.com/drive..../folders/1NEs0rpFelf

Timestamp for the topics covered in this Machine Learning Course video:

00:00 Introduction
4:22 AI vs ML vs DL
9:40 Types of Machine Learning
16:05 Supervised Learning & its Types
21:34 Unsupervised Learning & its Types
27:55 Deep Learning
36:11 Google Colaboratory - basics
45:53 Python Basics
1:08:36 Basic Data types in Python
1:28:40 List, Tuple, Set, Dictionary
1:55:36 Operators in Python
2:14:50 If Else Statement in Python
2:28:42 Loops in Python
2:44:20 Functions in Python
2:59:16 Numpy Tutorial
3:44:03 Pandas Tutorial
4:33:06 Matplotlib Tutorial
5:01:25 Seaborn Tutorial
5:37:34 Data Collection for ML
5:50:46 Importing datasets through Kaggle API
6:05:15 Handling Missing Values
6:29:58 Data Standardization
6:49:30 Label Encoding
7:06:56 Train Test Split
7:19:16 Handling Imbalanced Dataset
7:38:24 Feature Extraction of Text data
7:52:03 Numerical dataset processing
8:11:42 Textual data Processing
8:47:12 ML Use case 1: Rock vs Mine Prediction
9:35:35 ML Use case 2: Diabetes Prediction
10:38:53 ML Use case 3: Spam Mail Prediction


Machine Learning Projects Playlist: https://youtube.com/playlist?l....ist=PLfFghEzKVmjvuSA

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GAN Theft Auto is a Generative Adversarial Network that recreates the Grand Theft Auto 5 environment. It is created using a GameGAN fork based on NVIDIA's GameGAN research.

With GAN Theft Auto, the neural network *is* the environment and you can play within it.

Github: https://github.com/sentdex/GANTheftAuto/

Unboxing and reviewing the DGX Station A100 80GB: https://www.youtube.com/watch?v=0mAesfFt4us

Neural Networks from Scratch book: https://nnfs.io

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