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Udemy - Generative Adversarial Network (GAN) from scratch PyTorch

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Udemy - Generative Adversarial Network (GAN) from scratch PyTorch (Size: 3.8 GB)
  1. 1-Introduction DCGAN with TensorFlow.mp4 25.8 MB
  1. 1-Introduction DCGAN with TensorFlow.srt 3.4 KB
  1. Conditional GAN Introduction - from Scratch with PyTorch.mp4 4.8 MB
  1. Conditional GAN Introduction - from Scratch with PyTorch.srt 921.6 B
  1. CycleGAN Paper Architecture Explanations.mp4 294.8 MB
  1. CycleGAN Paper Architecture Explanations.srt 39.3 KB
  1. CycleGAN from Scratch with PyTorch.mp4 796.7 MB
  1. CycleGAN from Scratch with PyTorch.srt 134.1 KB
  1. DCGAN From Scratch with PyTorch.mp4 320.3 MB
  1. DCGAN From Scratch with PyTorch.srt 66.2 KB
  1. Introduction BiCycleGAN from Scratch with PyTorch.mp4 3.8 MB
  1. Introduction BiCycleGAN from Scratch with PyTorch.srt 204.8 B
  1. Introduction WGAN with Gradient_Penalty from Scratch with PyTorch.mp4 10.2 MB
  1. Introduction WGAN with Gradient_Penalty from Scratch with PyTorch.srt 614.4 B
  1. WGAN Architecture Paper Explanation.mp4 117.4 MB
  1. WGAN Architecture Paper Explanation.srt 34 KB
  1. WGAN Without Gradient Penalty from Scratch with PyTorch.mp4 238.6 MB
  1. WGAN Without Gradient Penalty from Scratch with PyTorch.srt 43.9 KB
  1.1 CycleGAN_From_Scratch_PyTorch_FINAL_Entire_NB.ipynb 1.9 MB
  1.1 DCGAN_ONLY_train.ipynb 1.3 KB
  1.1 Final_Training_Only.ipynb 614.4 B
  1.2 cyclegan.py 4.9 KB
  1.2 dcgan.py 4.4 KB
  1.2 train.py 5.6 KB
  1.3 Final_Training_Only.ipynb 921.6 B
  1.3 train.py 6.2 KB
  1.3 utils.py 1 KB
  1.4 WGAN_Pytorch_From_Scratch_Full_Notebook.ipynb 32.3 KB
  1.4 train.py 14.9 KB
  1.4 utils.py 1.7 KB
  1.5 utils.py 5.5 KB
  1.5 wgan.py 2.3 KB
  2. 2-Full Implementations - DCGAN from Scratch With TensforFlow CelebA_Dataset.mp4 469.7 MB
  2. 2-Full Implementations - DCGAN from Scratch With TensforFlow CelebA_Dataset.srt 78.1 KB
  2. Full Implementation - BiCycleGAN from Scratch with PyTorch.mp4 456.3 MB
  2. Full Implementation - BiCycleGAN from Scratch with PyTorch.srt 68.1 KB
  2. Full Implementation - Conditional GAN From Scratch with PyTorch.mp4 311.2 MB
  2. Full Implementation - Conditional GAN From Scratch with PyTorch.srt 57.7 KB
  2. Full Implementation WGAN with Gradient_Penalty from Scratch with PyTorch.mp4 529.3 MB
  2. Full Implementation WGAN with Gradient_Penalty from Scratch with PyTorch.srt 100.9 KB
  2.1 BiCycleGAN_Toward_Multimodal_Image-to-Image_Translation.ipynb 42.1 KB
  2.1 DCGAN_with_Tensorflow_Keras_Celeb_A_Dataset.ipynb 173.1 KB
  2.1 Only_Tranining_Final.ipynb 3.6 KB
  2.1 conditional_gan.py 2.6 KB
  2.2 Only_Tranining_Final_CGAN.ipynb 13.6 KB
  2.3 train.py 8.1 KB
  2.4 utils.py 4.1 KB
  2.5 wgan_gp.py 3.3 KB
  3. 3 Conv2dTranspose Explanations for DCGAN's Generator Function Filter-Kernel_Size.mp4 266 MB
  3. 3 Conv2dTranspose Explanations for DCGAN's Generator Function Filter-Kernel_Size.srt 47.3 KB
  3.1 DCGAN_Generator_Function_Understanding_Filter_Size_and_Input_Shape.ipynb 49.9 KB
  BiCycleGAN_Toward_Multimodal_Image-to-Image_Translation.ipynb 42.1 KB
  Bonus Resources.txt 409.6 B
  CycleGAN_From_Scratch_PyTorch_FINAL_Entire_NB.ipynb 1.9 MB
  DCGAN_Generator_Function_Understanding_Filter_Size_and_Input_Shape.ipynb 49.9 KB
  DCGAN_ONLY_train.ipynb 1.3 KB
  DCGAN_with_Tensorflow_Keras_Celeb_A_Dataset.ipynb 173.1 KB
  Final_Training_Only.ipynb 614.4 B
  Get Bonus Downloads Here.url 204.8 B
  Only_Tranining_Final.ipynb 3.6 KB
  Only_Tranining_Final_CGAN.ipynb 3.6 KB
  WGAN_Pytorch_From_Scratch_Full_Notebook.ipynb 32.3 KB
  conditional_gan.py 2.6 KB
  cyclegan.py 4.9 KB
  dcgan.py 4.4 KB
  train.py 5.6 KB
  utils.py 1 KB
  wgan.py 2.3 KB
  wgan_gp.py 3.3 KB
  ▲ 80 total files

Description


Generative Adversarial Network (GAN) from scratch | PyTorch
https://DevCourseWeb.com

Published 06/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 14 lectures (9h 2m) | Size: 3.85 GB

Its a code heavy and rather in-depth course to master Generative Adversarial Network implementation

What you'll learn
Learn how the basic principles of generative models work
Build & Implement a GAN from scratch (Generative Adversarial Network) in Pytorch and Tensorflow
How to improve the training stability of GANs
Under the hood understanding of the Generator and Discriminator Mechanism

Requirements
Basic Python, Basic Understanding of CNN, Convolutional Neural Network
Basic conceptes of deep learning and Neural Network flow

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