Generative AI, from GANs to CLIP, with Python and Pytorch
- Offered byUDEMY
Generative AI, from GANs to CLIP, with Python and Pytorch at UDEMY Overview
Duration | 10 hours |
Total fee | ₹399 |
Mode of learning | Online |
Credential | Certificate |
Generative AI, from GANs to CLIP, with Python and Pytorch at UDEMY Highlights
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Generative AI, from GANs to CLIP, with Python and Pytorch at UDEMY Course details
- 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.
- Examples: We will learn to edit the clothes of a person in a picture by combining a segmentation architecture with the Stable Diffusion generative model
- Special Bonus Final Section: experience a guided visualization to exercise the generative model in your head while you learn many things about neural networks
- 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.____________________________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!
Generative AI, from GANs to CLIP, with Python and Pytorch at UDEMY Curriculum
The generative AI revolution
The roadmap, from basic to advanced and beyond
Javier sends greetings from his spacecraft
The generative revolution: coming home
The present and future of AI is generative
Applications of generative AI
Latent spaces and representation learning
Navigating latent spaces
GANS: Generative Adversarial Networks
Benefits and possibilities of Generative AI
Coming home: generative AI and human nature
Javier sings a song dedicated to generative AI
Coding a basic generative architecture
Javier introduces section 2 from his spacecraft
Understanding the battle between generator and discriminator
Understanding Cross Entropy in depth
Understanding the equation to calculate the discriminator loss
Understanding the equation to calculate the generator loss
(Optional) Google Colab Tutorial
Coding: importing libraries and declaring a visualization function
Coding: hyperparameters and the DataLoader
Coding: the generator class
Coding: the discriminator class
Coding: the optimizer and testing the generator
Coding: the loss values of generator and discriminator
Coding: main training loop, discriminator part
Coding: main training loop, generator and stats
Coding: running the training
Coding: results and conclusions
Coding an advanced generative architecture
Javier introduces section 3 from his spacecraft
Challenges and issues of the basic GAN
The Wasserstein Loss
The Gradient Penalty
Coding: setting up libraries and parameters
Coding: Login and setup of the Wandb stats library
Coding: Beginning the generator
Coding: Understanding convolutions
Coding: The generator class
Coding: The critic class
Coding: Alternative way to initialize parameters (optional)
Coding: Loading the CelebA dataset
Coding: Declaring dataset, dataloader and optimizers
Coding: the gradient penalty
Coding: saving and loading checkpoints
Coding: training loop - critic training
Coding: training loop - generator training
Coding: stats and fixing issues
Coding: reviewing the code before running the training
Coding: running the training
Coding: results after a few epochs
Coding: results after a few more epochs
Coding: results getting better and better
Coding: morphing between points in latent space
Coding: more morphing
Generating images from text by combining two advanced architectures
Javier introduces section 4 from his spacecraft
Multimodal generation, an incredible adventure
Coding: importing the libraries
Coding: helper functions and hyperparameters
Coding: Setting up the CLIP model
Coding: Setting up the Generative transformer model
Coding: Setting up the latent space parameters to be optimized
Coding: encode the text prompts through CLIP
Coding: creating crops from the generated image
Coding: a function to display generated images and crops
Coding: optimizing the latent space parameters
Coding: the training loop
Coding: running the training
Coding: interpolating between points in the latent space
Coding: creating a video of the interpolations and general review
Coding: creating variations of the code
Coding: Davinci Sfumato: Tweaking the code to create a new kind of texture
Coding: Davinci Sfumato: reflecting about the process
Final greetings from the spacecraft
Editing people's clothes by combining segmentation and generative AI models
Intro: people's clothes replacement and editing using Generative AI
Coding: Setting up libraries and the segmentation model
Coding: Setting up the Stable Diffusion generative model
Coding: Loading a picture and running the segmentation process to produce masks
Coding: Visualizing the generated masks
Coding: Inpainting, running and experimenting with the Stable Diffusion model
Coding: Guide the segmentation process with text prompts
Coding: run the generative model in this alternative setup
Ending of the section
Bonus: Activating the Generative Model of your own mind
A guided visualization experience to exercise the generative model in your head
Intro to the journey to the center of the neuron
The container, the salty ocean and the 150000 cortical columns
Visualizing the pyramidal neuron
The Synapse, visualizing the input-output interface
Biological vs Artificial Neurons: Inputs, Outputs, Speed, etc
Learning in biological and artificial neurons
Planning, decision making and world models
Efficiency: sparsity in biological vs artificial networks
Consciousness: within the neurons
The future, towards AGI / ASI
Generative AI, from GANs to CLIP, with Python and Pytorch at UDEMY Faculty details
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