[ FreeCourseWeb ] Udemy - Generative A.I., from GANs to CLIP, with Python and Pytorch

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[ FreeCourseWeb.com ] Udemy - Generative A.I., from GANs to CLIP, with Python and Pytorch
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 01 The generative revolution
    • 001 The roadmap, from basic to advanced and beyond.en.srt (2.5 KB)
    • 001 The roadmap, from basic to advanced and beyond.mp4 (9.8 MB)
    • 002 Javier sends greetings from his spacecraft.en.srt (1.7 KB)
    • 002 Javier sends greetings from his spacecraft.mp4 (28.3 MB)
    • 003 The generative revolution_ coming home.en.srt (6.5 KB)
    • 003 The generative revolution_ coming home.mp4 (56.0 MB)
    • 004 The present and future of A.I is generative.en.srt (8.7 KB)
    • 004 The present and future of A.I is generative.mp4 (60.8 MB)
    • 005 Applications of generative AI.en.srt (5.5 KB)
    • 005 Applications of generative AI.mp4 (45.1 MB)
    • 006 Latent spaces and representation learning.en.srt (12.3 KB)
    • 006 Latent spaces and representation learning.mp4 (84.8 MB)
    • 007 Navigating latent spaces.en.srt (12.4 KB)
    • 007 Navigating latent spaces.mp4 (106.5 MB)
    • 008 GANS_ Generative Adversarial Networks.en.srt (8.9 KB)
    • 008 GANS_ Generative Adversarial Networks.mp4 (64.2 MB)
    • 009 Benefits and possibilities of Generative A.I.en.srt (7.6 KB)
    • 009 Benefits and possibilities of Generative A.I.mp4 (64.7 MB)
    • 010 Coming home_ generative A.I and human nature.en.srt (5.6 KB)
    • 010 Coming home_ generative A.I and human nature.mp4 (41.7 MB)
    • 010 images-used-presentation-creative-commons.txt (0.8 KB)
    • 011 Javier sings a song dedicated to generative A.I.en.srt (1.2 KB)
    • 011 Javier sings a song dedicated to generative A.I.mp4 (40.2 MB)
    • external-assets-links.txt (0.1 KB)
    02 Coding a basic generative architecture
    • 001 Javier introduces section 2 from his spacecraft.en.srt (0.7 KB)
    • 001 Javier introduces section 2 from his spacecraft.mp4 (10.3 MB)
    • 002 Understanding the battle between generator and discriminator.en.srt (12.0 KB)
    • 002 Understanding the battle between generator and discriminator.mp4 (48.4 MB)
    • 003 Understanding Cross Entropy in depth.en.srt (14.8 KB)
    • 003 Understanding Cross Entropy in depth.mp4 (95.7 MB)
    • 004 Understanding the equation to calculate the discriminator loss.en.srt (8.3 KB)
    • 004 Understanding the equation to calculate the discriminator loss.mp4 (26.6 MB)
    • 005 Understanding the equation to calculate the generator loss.en.srt (4.2 KB)
    • 005 Understanding the equation to calculate the generator loss.mp4 (12.4 MB)
    • 006 (Optional) Google Colab Tutorial.en.srt (16.8 KB)
    • 006 (Optional) Google Colab Tutorial.mp4 (47.9 MB)
    • 007 Coding_ importing libraries and declaring a visualization function.en.srt (19.8 KB)
    • 007 Coding_ importing libraries and declaring a visualization function.mp4 (56.3 MB)
    • 008 Coding_ hyperparameters and the DataLoader.en.srt (13.5 KB)
    • 008 Coding_ hyperparameters and the DataLoader.mp4 (45.0 MB)
    • 009 Coding_ the generator class.en.srt (10.6 KB)
    • 009 Coding_ the generator class.mp4 (51.8 MB)
    • 010 Coding_ the discriminator class.en.srt (8.6 KB)
    • 010 Coding_ the discriminator class.mp4 (38.1 MB)
    • 011 Coding_ the optimizer and testing the generator.en.srt (10.2 KB)
    • 011 Coding_ the optimizer and testing the generator.mp4 (37.4 MB)
    • 012 Coding_ the loss values of generator and discriminator.en.srt (11.1 KB)
    • 012 Coding_ the loss values of generator and discriminator.mp4 (42.4 MB)
    • 013 Coding_ main training loop, discriminator part.en.srt (7.6 KB)
    • 013 Coding_ main training loop, discriminator part.mp4 (22.7 MB)
    • 014 Coding_ main training loop, generator and stats.en.srt (8.9 KB)
    • 014 Coding_ main training loop, generator and stats.mp4 (38.8 MB)
    • 015 Coding_ running the training.en.srt (2.3 KB)
    • 015 Coding_ running the training.mp4 (6.8 MB)
    • 016 Coding_ results and conclusions.en.srt (1.6 KB)
    • 016 Coding_ results and conclusions.mp4 (4.9 MB)
    • 027 Basic_GAN_Generative_A_I_course_by_Ideami.ipynb (8.5 KB)
    03 Coding an advanced generative architecture
    • 001 Javier introduces section 3 from his spacecraft.en.srt (1.3 KB)
    • 001 Javier introduces section 3 from his spacecraft.mp4 (20.4 MB)
    • 002 Challenges and issues of the basic GAN.en.srt (8.8 KB)
    • 002 Challenges and issues of the basic GAN.mp4 (29.0 MB)
    • 003 The Wasserstein Loss.en.srt (10.5 KB)
    • 003 The Wasserstein Loss.mp4 (32.3 MB)
    • 004 The Gradient Penalty.en.srt (7.6 KB)
    • 004 The Gradient Penalty.mp4 (30.0 MB)
    • 005 Coding_ setting up libraries and parameters.en.srt (19.8 KB)
    • 005 Coding_ setting up libraries and parameters.mp4 (50.7 MB)
    • 006 Coding_ Login and setup of the Wandb stats library.en.srt (6.7 KB)
    • 006 Coding_ Login and setup of the Wandb stats library.mp4 (23.6 MB)
    • 007 Coding_ Beginning the generator.en.srt (6.9 KB)
    • 007 Coding_ Beginning the generator.mp4 (20.7 MB)
    • 008 Coding_ Understanding convolutions.en.srt (19.7 KB)
    • 008 Coding_ Understanding convolutions.mp4 (104.4 MB)
    • 009 Coding_ The generator class.en.srt (21.7 KB)
    • 009 Coding_ The generator class.mp4 (83.4 MB)
    • 010 Coding_ The critic class.en.srt (17.6 KB)
    • 010 Coding_ The critic class.mp4 (53.7 MB)
    • 011 Coding_ Alternative way to initialize parameters (optional).en.srt (4.6 KB)
    • 011 Coding_ Alternative way to initialize parameters (optional).mp4 (14.7 MB)
    • 012 Coding_ Loading the CelebA dataset.en.srt (10.2 KB)
    • 012 Coding_ Loading the CelebA dataset.mp4 (39.9 MB)
    • 013 Coding_ Declaring dataset, dataloader and optimizers.en.srt (24.9 KB)
    • 013 Coding_ Declaring dataset, dataloader and optimizers.mp4 (85.1 MB)
    • 014 Coding_ the gradient penalty.en.srt (10.6 KB)
    • 014 Coding_ the gradient penalty.mp4 (30.9 MB)
    • 015 Coding_ saving and loading checkpoints.en.srt (9.7 KB)
    • 015 Coding_ saving and loading checkpoints.mp4 (36.0 MB)
    • 016 Coding_ training loop - critic training.en.srt (7.8 KB)
    • 016 Coding_ training loop - critic training.mp4 (23.6 MB)
    • 017 Coding_ training loop - generator training.en.srt (2.2 KB)
    • 017 Coding_ training loop - generator training.mp4 (6.5 MB)
    • 018 Coding_ stats and fixing issues.en.srt (13.6 KB)
    • 018 Coding_ stats and fixing

Description

Generative A.I., from GANs to CLIP, with Python and Pytorch



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 71 lectures (8h) | Size: 2.3 GB
Learn to code the most creative and exciting A.I. architectures, generative networks, from basic to advanced and beyond
What you'll learn:
How to code generative A.I architectures from scratch using Python and Pytorch
How generative architectures work, in great depth, from GANs to multimodal A.I, understanding every little detail in the process
In addition to the coding, every section begins with an in-depth review of the key concepts related to these architectures
Examples: We will code a generative network that produces human faces, and also combine two advanced networks to transform text prompts into amazing images. We will also understand in depth the details

Requirements
Basic knowledge of python. It's enough with the very basics, as we will code every little thing together, line by line
Access to an internet connection, as we will use the free online Google Colab service to code together
Plenty of enthusiasm as we will go deep into every little detail, let's do it! :)

Description
Generative A.I. is the present and future of A.I. and deep learning, and it will touch every part of our lives. It is the part of A.I that is closer to our unique human capability of creating, imagining and inventing. By doing this course, you gain advanced knowledge and practical experience in the most promising part of A.I., deep learning, data science and advanced technology.

The course takes you on a fascinating journey in which you learn gradually, step by step, as we code together a range of generative architectures, from basic to advanced, until we reach multimodal A.I, where text and images are connected in incredible ways to produce amazing results.

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[ FreeCourseWeb ] Udemy - Generative A.I., from GANs to CLIP, with Python and Pytorch

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2.9 GB
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[ FreeCourseWeb ] Udemy - Generative A.I., from GANs to CLIP, with Python and Pytorch

Torrent hash: ADB3ACFBDA2EE3BFF75AE3D603F1D9E8138B5AF4