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

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

4.52 (15446 reviews)

Udemy

platform

English

language

Data Science

What you will 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 Section: Journey to the latent space of a neural network, learn in depth how the networks that power Generative AI learn their mappings

Special Bonus Section: Experience a guided visualization to exercise the generative model in your head while you learn many things about neural networks

Course Gallery

Generative AI, from GANs to CLIP, with Python and Pytorch – Screenshot 1 — Screenshot 1 – Generative AI, from GANs to CLIP, with Python and Pytorch

Generative AI, from GANs to CLIP, with Python and Pytorch – Screenshot 2 — Screenshot 2 – Generative AI, from GANs to CLIP, with Python and Pytorch

Generative AI, from GANs to CLIP, with Python and Pytorch – Screenshot 3 — Screenshot 3 – Generative AI, from GANs to CLIP, with Python and Pytorch

Generative AI, from GANs to CLIP, with Python and Pytorch – Screenshot 4 — Screenshot 4 – Generative AI, from GANs to CLIP, with Python and Pytorch

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Comidoc Review

Our Verdict

Generative AI, from GANs to CLIP, with Python and PyTorch is an engaging course that covers a wide range of modern AI networks. While its depth may prove challenging for beginners and the pacing can be uneven, its unique approach to teaching generative models makes it a valuable addition to any AI learner's toolkit.

What We Liked

Excellent coverage of generative AI architectures like GANs, Variational Autoencoders (VAEs), and CLIP.
The instructor's enthusiasm and knowledge make for an engaging learning experience.
Code-along approach helps to solidify understanding of the material.
Bonus sections on Latent Space and Guided Visualization offer unique insights into neural network learnings.

Potential Drawbacks

The course may be challenging for those without prior exposure to deep learning and PyTorch.
Some sections could benefit from more detailed explanations, particularly with regards to the reasoning behind certain code decisions.
A few technical issues were reported, such as outdated library versions and occasionally cryptic variable names.
Slide-based content can feel overly dense and static, making it difficult to follow at times.