Generative AI, from GANs to CLIP, with Python and Pytorch

Generative AI, from GANs to CLIP, with Python and Pytorch - Printable Version

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Generative AI, from GANs to CLIP, with Python and Pytorch - BaDshaH - 10-05-2023

[Image: 68b55ff0d8607a2ac9491bd41c3b4144.jpg]

Last updated 9/2023
Duration: 10h 34m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 4.32 GB
Genre: eLearning | Language: English

Learn to code with the most creative and exciting AI architectures, generative AI networks, from basic to advanced

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.
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

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! Smile

Description
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!

Who this course is for
People interested in using A.I and deep learning to generate, imagine and create new things
People interested in generative adversarial networks and other advanced A.I generative architectures
People interested in how A.I can combine different modalities (text, images) to create new things (multimodal A.I.)
People interested in learning to code the type of advanced A.I architectures that are the present and future of the field

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