Master Generative AI Stack: practical handbook

Author(s): Nail Valiyev

Originally published on Towards AI.

AI has been on a remarkable journey since its inception in the 1950s, but the last decade has truly transformed the landscape. Unlike anything before, Generative AI has the astonishing ability to create images, text, and code that resemble human-like creations. However, to truly leverage its potential, a solid and well-integrated framework is essential. Let’s walk through its workings and how it can be applied in practical ways.

The diagram above shows that deep learning is a subset of Machine Learning. Traditional ML algorithms mostly fall into either supervised learning — when you have the target labels to train the prediction model on; or unsupervised learning when there are no target labels.

It could be helpful to go over some ML jargon and get a high-level idea of popular ML algorithms. Since AI today uses Deep Learning, you can jump straight into DL. You will likely learn the essential ML concepts along the way, and you may fill in the gaps in your understanding if needed.

Source: https://www.labellerr.com/blog/supervised-vs-unsupervised-learning-whats-the-difference/

Neural networks are the algorithm behind deep learning, including Gen AI we see today. It works incredibly well for unstructured data like text and images. A neural network, in itself, is pretty simple and maybe even… Read the full blog for free on Medium.

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Published via Towards AI