Generative AI as a Subset of Deep Learning
1. Generative AI as a Subset of Deep Learning
while machine learning is a broad field that encompasses many different techniques deep learning is a type of machine learning that uses artificial neural networks allowing them to process more complex patterns than machine learning artificial neural networks are inspired by the human brain pretty cool huh like your brain they are made up of many interconnected nodes or neurons that can learn to perform tasks by processing data and making predictions deep learning models typically have many layers of neurons which allows them to learn more complex patterns than traditional machine learning models neural networks can use both labeled and unlabelled data this is called semi-supervised learning in semi-supervised learning a neural network is trained on a small amount of labeled data and a large amount of unlabeled data the labeled data helps the neural network to learn the basic concepts of the tasks while the unlabeled data helps the neural network to generalize to new examples now we finally get to where generative AI fits into this AI discipline gen AI is a subset of deep learning which means it uses artificial neural networks can process both labeled and unlabeled data using supervised unsupervised and semisupervised methods large language models are also a subset of deep learning see I told you I'd bring it all back to geni good job me deep learning models or machine learning models in general can be divided into two types generative and discriminative a discriminative model is a type of model that is used to classify or predict labels for data points discriminative models are typically trained on a data set of labeled data points and they learn the relationship between the features of the data points and the labels once a discriminative model is trained it can be used to predict the label for new data points a generative model generates new data instances based on a learned probability distribution of existing data generative models generate new content take this example here the discriminative model learns the conditional probability distribution or the probability of why our output given X our input that this is a dog and classifies it as a dog and not a cat which is great because I'm allergic to cats the generative model learns The Joint probability distribution or the probability of X and Y P of x y and predicts the conditional probability that this is a dog and can then generate a picture of a dog good boy I'm going to name him Fred to summarize generative models can generate new data instances and discriminative models discriminate between different kind kinds of data instances one more quick example the top image shows a traditional machine learning model which attempts to learn the relationship between the data and the label or what you want to predict the bottom image shows a generative AI model which attempts to learn patterns on content so that it can generate new content2. Let's practice!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.