05 Oct Types of Deep Learning
We learned about Deep Learning in the previous lessons. Now, we will see the types of Deep Learning: Discriminative and Generative. Let us understand them one by one:
Discriminative
This type of Deep Learning is used to classify or predict. It discriminates between different kinds of data instances. It requires labeled data for training
- Applications: Classification tasks, e.g., spam detection, image recognition
- Algorithms: Logistic Regression, Support Vector Machines (SVM), Neural Networks
- Discriminative model algorithm application: Logistic regression predicts if an email is spam or not by learning the boundary between spam and non-spam emails.
- Example: Classify images as a dog or a cat.
Generative AI
Under this type of Deep Learning, new data is generated that is like the data it was trained on. Generates new data instances. It can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods. Generative AI is a subset of Deep Learning. It uses AI neural networks
- Applications: Data generation, e.g., creating images, text, music
- Algorithms: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Naive Bayes
- Generative Model algorithm application: GANs can generate realistic-looking images of faces by learning the distribution of real face images.
- Example: Generate a dog image.
Refer to our Generative AI free tutorial.
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