Need Guidance for Learning Generative AI

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Hi everyone,

I’m trying to learn Generative AI. I have knowledge of Python, some math for machine learning, and a good understanding of computer vision fundamentals.

What should I focus on next? Any guidance would be appreciated.

Thanks!

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Hi @Mosaab_EL_Bouamrani ,

To learn Generative AI, I would recommend you to build on your Python and machine learning foundation by studying key models like GANs, VAEs, and Diffusion Models for image generation, as well as Transformers for text generation. Also, gain hands-on experience by implementing these models with frameworks like PyTorch or TensorFlow and experimenting with pre-trained tools like Hugging Face. Focus on projects like image synthesis, text generation, or style transfer to apply your knowledge. Stay updated with research and communities like arXiv or Hugging Face forums, and continuously refine your skills through practice and exploration.

@saou_a
DL Mentor

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Getting started with Generative AI is a great move—it’s one of the fastest-growing fields in artificial intelligence today. Whether you’re a developer, student, or enthusiast, the learning curve can be steep at first, but very rewarding.

Start with the basics of machine learning and deep learning, as these are foundational to understanding how generative models work. Coursera, edX, and free resources like Google’s Machine Learning Crash Course are excellent places to begin.

Next, dive into frameworks like TensorFlow, PyTorch, and Hugging Face Transformers. These are widely used in building and training models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and LLMs (Large Language Models).

For hands-on practice, try replicating popular projects like image generation, text summarization, or chatbot creation. Open-source platforms like GitHub and Google Colab offer plenty of pre-built notebooks you can explore and modify.

Stay updated by following communities on Reddit, LinkedIn, and specialized Discord groups. You might also want to explore research papers via arXiv or attend webinars on Generative AI.

Most importantly, be patient. Start small, experiment often, and build a portfolio of mini-projects to apply your knowledge. The field is evolving fast—there’s never been a better time to get involved!