Generative AI Roadmap Introduction
Generative AI, also known as generative adversarial networks (GANs), is a subfield of artificial intelligence that focuses on creating models capable of generating new and realistic data. This roadmap aims to provide an overview of the key concepts and steps involved in understanding and working with generative AI.
Prerequisites
Before diving into generative AI, it is important to have a strong foundation in machine learning and deep learning concepts. Familiarity with neural networks, training algorithms, and optimization techniques is necessary to grasp the underlying principles of generative AI.
Step 1: Understanding Generative Models What are Generative Models?
Generative models are algorithms that learn the underlying probability distribution of a given dataset. They can generate new samples that are similar to the training data and capture the underlying patterns and structure of the data.
Types of Generative Models
There are various types of generative models, including but not limited to: •Autoregressive Models
: These models generate data by modeling the conditional probability of each data point given previous data points. •Variational Autoencoders (VAEs)
: VAEs learn a low-dimensional representation of the input data and generate new samples by sampling from this latent space. •Generative Adversarial Networks (GANs)
: GANs consist of two neural networks, a generator and a discriminator, which compete against each other to generate realistic data. •Flow-based Models
: Flow-based models directly learn the mapping from the input space to the target space.
Learning Resources
To understand generative models in depth, the following resources are recommended: • Research papers on generative models, such as those by Ian Goodfellow and his colleagues. • Online courses and tutorials on generative AI, such as the ones offered by Coursera and