I am completely new to the field and keep hearing about things like ChatGPT and Midjourney. Can someone explain what exactly generative AI models are in simple terms? I want to know the core differences between traditional AI and this new tech, and what specific math or coding foundations I should study first to eventually build my own applications.
3 answers
To start, think of traditional AI as a "judge" that categorizes data, while generative AI models are "creators" that produce new content based on patterns they've learned. For a beginner, I recommend starting with Python programming and basic probability. Once you're comfortable, dive into Natural Language Processing (NLP) fundamentals. You don't need a PhD in math, but understanding linear algebra and how neural networks function is crucial. Platforms like Hugging Face offer great open-source libraries that let you experiment with pre-trained models without needing massive computing power or deep architectural knowledge right away.
Do you think it's better for a novice to start with the theory of transformers, or just jump into API calls for existing generative AI models?
I found that learning about GANs (Generative Adversarial Networks) first helped me visualize how two generative AI models "compete" to create realistic images.
Michael is right. Visualizing the generator and discriminator makes the abstract concept of high-dimensional data much easier for a beginner to grasp.
I suggest jumping into the APIs first. Getting a quick win by seeing the model respond to your code provides the motivation needed to grind through the difficult transformer theory later on.