Generative Adversarial Network (GAN) from scratch PyTorch --> [ DevCourseWeb ]


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Torrent File Content (80 files)


[ DevCourseWeb.com ] Udemy - Generative Adversarial Network (GAN) from scratch PyTorch
     Get Bonus Downloads Here.url -
182 bytes

    ~Get Your Files Here !
          1. Conditional GAN From Scratch with PyTorch/1. Conditional GAN Introduction - from Scratch with PyTorch.mp4 -
4.82 MB

          1. Conditional GAN From Scratch with PyTorch/1. Conditional GAN Introduction - from Scratch with PyTorch.srt -
909 bytes

          1. Conditional GAN From Scratch with PyTorch/2. Full Implementation - Conditional GAN From Scratch with PyTorch.mp4 -
311.24 MB

          1. Conditional GAN From Scratch with PyTorch/2. Full Implementation - Conditional GAN From Scratch with PyTorch.srt -
57.67 KB

          1. Conditional GAN From Scratch with PyTorch/2.1 conditional_gan.py -
2.55 KB

          1. Conditional GAN From Scratch with PyTorch/2.2 Only_Tranining_Final_CGAN.ipynb -
13.62 KB

          1. Conditional GAN From Scratch with PyTorch/2.3 train.py -
8.76 KB

          1. Conditional GAN From Scratch with PyTorch/2.4 utils.py -
2.69 KB

          2. BiCycleGAN from Scratch with PyTorch/1. Introduction BiCycleGAN from Scratch with PyTorch.mp4 -
3.79 MB

          2. BiCycleGAN from Scratch with PyTorch/1. Introduction BiCycleGAN from Scratch with PyTorch.srt -
226 bytes

          2. BiCycleGAN from Scratch with PyTorch/2. Full Implementation - BiCycleGAN from Scratch with PyTorch.mp4 -
456.29 MB

          2. BiCycleGAN from Scratch with PyTorch/2. Full Implementation - BiCycleGAN from Scratch with PyTorch.srt -
68.1 KB

          2. BiCycleGAN from Scratch with PyTorch/2.1 BiCycleGAN_Toward_Multimodal_Image-to-Image_Translation.ipynb -
42.14 KB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/1. 1-Introduction DCGAN with TensorFlow.mp4 -
25.76 MB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/1. 1-Introduction DCGAN with TensorFlow.srt -
3.43 KB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/2. 2-Full Implementations - DCGAN from Scratch With TensforFlow CelebA_Dataset.mp4 -
469.7 MB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/2. 2-Full Implementations - DCGAN from Scratch With TensforFlow CelebA_Dataset.srt -
78.07 KB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/2.1 DCGAN_with_Tensorflow_Keras_Celeb_A_Dataset.ipynb -
173.13 KB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/3. 3 Conv2dTranspose Explanations for DCGAN's Generator Function Filter-Kernel_Size.mp4 -
266.04 MB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/3. 3 Conv2dTranspose Explanations for DCGAN's Generator Function Filter-Kernel_Size.srt -
47.28 KB

          3. DCGAN from Scratch with TensorFlow - Generate fake Faces from CelebA Dataset/3.1 DCGAN_Generator_Function_Understanding_Filter_Size_and_Input_Shape.ipynb -
49.88 KB

          4. DCGAN From Scratch with PyTorch/1. DCGAN From Scratch with PyTorch.mp4 -
320.28 MB

          4. DCGAN From Scratch with PyTorch/1. DCGAN From Scratch with PyTorch.srt -
66.2 KB

          4. DCGAN From Scratch with PyTorch/1.1 DCGAN_ONLY_train.ipynb -
1.35 KB

          4. DCGAN From Scratch with PyTorch/1.2 dcgan.py -
4.38 KB

          4. DCGAN From Scratch with PyTorch/1.3 train.py -
6.2 KB

          4. DCGAN From Scratch with PyTorch/1.4 utils.py -
1.72 KB

          5. CycleGAN Paper Architecture Explanations/1. CycleGAN Paper Architecture Explanations.mp4 -
294.84 MB

          5. CycleGAN Paper Architecture Explanations/1. CycleGAN Paper Architecture Explanations.srt -
39.33 KB

          6. CycleGAN from Scratch with PyTorch/1. CycleGAN from Scratch with PyTorch.mp4 -
796.7 MB

          6. CycleGAN from Scratch with PyTorch/1. CycleGAN from Scratch with PyTorch.srt -
134.09 KB

          6. CycleGAN from Scratch with PyTorch/1.1 CycleGAN_From_Scratch_PyTorch_FINAL_Entire_NB.ipynb -
1.87 MB

          6. CycleGAN from Scratch with PyTorch/1.2 cyclegan.py -
4.9 KB

          6. CycleGAN from Scratch with PyTorch/1.3 Final_Training_Only.ipynb -
875 bytes

          6. CycleGAN from Scratch with PyTorch/1.4 train.py -
14.9 KB

          6. CycleGAN from Scratch with PyTorch/1.5 utils.py -
5.49 KB

          7. WGAN Architecture Paper Explanation/1. WGAN Architecture Paper Explanation.mp4 -
117.36 MB

          7. WGAN Architecture Paper Explanation/1. WGAN Architecture Paper Explanation.srt -
34.04 KB

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1. WGAN Without Gradient Penalty from Scratch with PyTorch.mp4 -
238.63 MB

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1. WGAN Without Gradient Penalty from Scratch with PyTorch.srt -
43.89 KB

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1.1 Final_Training_Only.ipynb -
636 bytes

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1.2 train.py -
5.57 KB

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1.3 utils.py -
1009 bytes

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1.4 WGAN_Pytorch_From_Scratch_Full_Notebook.ipynb -
32.3 KB

          8. WGAN Without Gradient Penalty from Scratch with PyTorch/1.5 wgan.py -
2.28 KB

          9. WGAN with Gradient_Penalty from Scratch with PyTorch/1. Introduction WGAN with Gradient_Penalty from Scratch with PyTorch.mp4 -
10.23 MB

          9. WGAN with Gradient_Penalty from Scratch with PyTorch/1. Introduction WGAN with Gradient_Penalty from Scratch with PyTorch.srt -
621 bytes

          9. WGAN with Gradient_Penalty from Scratch with PyTorch/2. Full Implementation WGAN with Gradient_Penalty from Scratch with PyTorch.mp4 -
529.29 MB

          9. WGAN with Gradient_Penalty from Scratch with PyTorch/2. Full Implementation WGAN with Gradient_Penalty from Scratch with PyTorch.srt -
100.93 KB



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