Mô tả

September 2023: Update: Two new sections have been added recently. In Section 5 you will learn to edit the clothes of a person in a picture by programming a combination of a segmentation model with the Stable Diffusion generative model. The other new section is a final Bonus Extra. In this course you do programming of different generative models. In the new Section 6, you will be the generative model yourself. You will practice to exercise the generative model of your own head by doing a guided visualization journey with me, a journey to the center of a neuron. You will learn about biological and artificial neurons, as well as their learning and planning processes, while you exercise the generative model in your head, guided by the GPT-like generative model in my head.

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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.

At the beginning of each section, I explain the key concepts in great depth and then we code together, you and me, line by line, understanding everything, conquering together the challenge of building the most promising A.I architectures of today and tomorrow. After you complete the course, you will have a deep understanding of both the key concepts and the fine details of the coding process.

What a time to be alive! We are able to code and understand architectures that bring us home, home to our own human nature, capable of creating and imagining. Together, we will make it happen. Let's do it!

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6 sections

The generative AI revolution

11 lectures
The roadmap, from basic to advanced and beyond
01:57
Javier sends greetings from his spacecraft
01:15
The generative revolution: coming home
05:35
The present and future of AI is generative
06:48
Applications of generative AI
04:09
Latent spaces and representation learning
08:54
Navigating latent spaces
09:10
GANS: Generative Adversarial Networks
06:40
Benefits and possibilities of Generative AI
06:00
Coming home: generative AI and human nature
04:21
Javier sings a song dedicated to generative AI
02:07

Coding a basic generative architecture

16 lectures
Javier introduces section 2 from his spacecraft
00:27
Understanding the battle between generator and discriminator
08:58
Understanding Cross Entropy in depth
10:23
Understanding the equation to calculate the discriminator loss
05:19
Understanding the equation to calculate the generator loss
03:05
(Optional) Google Colab Tutorial
12:20
Coding: importing libraries and declaring a visualization function
13:48
Coding: hyperparameters and the DataLoader
09:15
Coding: the generator class
08:17
Coding: the discriminator class
06:34
Coding: the optimizer and testing the generator
07:35
Coding: the loss values of generator and discriminator
08:52
Coding: main training loop, discriminator part
06:12
Coding: main training loop, generator and stats
07:08
Coding: running the training
01:18
Coding: results and conclusions
01:03

Coding an advanced generative architecture

25 lectures
Javier introduces section 3 from his spacecraft
00:54
Challenges and issues of the basic GAN
06:54
The Wasserstein Loss
07:31
The Gradient Penalty
05:26
Coding: setting up libraries and parameters
13:27
Coding: Login and setup of the Wandb stats library
04:47
Coding: Beginning the generator
04:36
Coding: Understanding convolutions
13:18
Coding: The generator class
13:39
Coding: The critic class
13:08
Coding: Alternative way to initialize parameters (optional)
03:19
Coding: Loading the CelebA dataset
07:00
Coding: Declaring dataset, dataloader and optimizers
17:29
Coding: the gradient penalty
06:53
Coding: saving and loading checkpoints
07:05
Coding: training loop - critic training
05:18
Coding: training loop - generator training
01:31
Coding: stats and fixing issues
10:27
Coding: reviewing the code before running the training
05:25
Coding: running the training
04:16
Coding: results after a few epochs
03:11
Coding: results after a few more epochs
01:30
Coding: results getting better and better
02:07
Coding: morphing between points in latent space
12:01
Coding: more morphing
01:35

Generating images from text by combining two advanced architectures

19 lectures
Javier introduces section 4 from his spacecraft
00:58
Multimodal generation, an incredible adventure
12:01
Coding: importing the libraries
05:26
Coding: helper functions and hyperparameters
07:26
Coding: Setting up the CLIP model
03:20
Coding: Setting up the Generative transformer model
11:47
Coding: Setting up the latent space parameters to be optimized
06:35
Coding: encode the text prompts through CLIP
11:40
Coding: creating crops from the generated image
10:17
Coding: a function to display generated images and crops
03:30
Coding: optimizing the latent space parameters
08:49
Coding: the training loop
06:13
Coding: running the training
13:27
Coding: interpolating between points in the latent space
08:23
Coding: creating a video of the interpolations and general review
10:47
Coding: creating variations of the code
06:45
Coding: Davinci Sfumato: Tweaking the code to create a new kind of texture
07:21
Coding: Davinci Sfumato: reflecting about the process
04:24
Final greetings from the spacecraft
00:53

Editing people's clothes by combining segmentation and generative AI models

9 lectures
Intro: people's clothes replacement and editing using Generative AI
12:55
Coding: Setting up libraries and the segmentation model
10:16
Coding: Setting up the Stable Diffusion generative model
05:34
Coding: Loading a picture and running the segmentation process to produce masks
09:40
Coding: Visualizing the generated masks
07:28
Coding: Inpainting, running and experimenting with the Stable Diffusion model
12:26
Coding: Guide the segmentation process with text prompts
09:14
Coding: run the generative model in this alternative setup
06:07
Ending of the section
00:30

Bonus: Activating the Generative Model of your own mind

11 lectures
A guided visualization experience to exercise the generative model in your head
05:25
Intro to the journey to the center of the neuron
04:43
The container, the salty ocean and the 150000 cortical columns
07:58
Visualizing the pyramidal neuron
17:49
The Synapse, visualizing the input-output interface
06:01
Biological vs Artificial Neurons: Inputs, Outputs, Speed, etc
08:37
Learning in biological and artificial neurons
07:54
Planning, decision making and world models
12:32
Efficiency: sparsity in biological vs artificial networks
03:08
Consciousness: within the neurons
02:50
The future, towards AGI / ASI
02:51

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