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Generative AI: Principles and Types of Generative Models
Introduction:
Generative AI refers to systems that
can create new content, from text to images to music, mimicking human
creativity. Unlike traditional AI models that predict outcomes based on input
data, generative models produce original data similar to the examples they were
trained on. Understanding the principles and types of generative models is
essential to grasp the breadth of this transformative technology. Generative AI (GenAI) Courses Online
Principles
of Generative AI
At its core, generative AI
operates on the principle of learning patterns from a dataset and then using
these patterns to generate new, similar data. This involves two main phases:
training and generation. During training, the model learns the distribution of
the input data. In the generation phase, the model uses this learned distribution
to create new data instances. DataScience with Generative AI Course
Key
principles include:
Data Representation: Generative models learn to represent
data in a lower-dimensional space. This involves encoding complex data (like
images) into simpler forms (latent space), which can then be decoded back into
original-like data. Gen AI Course in Hyderabad
Probability Estimation: These models estimate the probability
distribution of the input data. This allows them to sample from this
distribution to create new data points that are statistically similar to the
training data.
Adversarial Training: Particularly in Generative Adversarial
Networks (GANs), the model involves two neural networks – a generator and a
discriminator – in a game-theoretic setup where the generator creates data to
fool the discriminator, which in turn gets better at distinguishing real from
fake data.
Types
of Generative Models
Generative Adversarial Networks (GANs): GANs consist of two networks, a
generator and a discriminator, that contest with each other. The generator
creates fake data, while the discriminator tries to identify whether the data
is real or generated. This adversarial process helps in creating highly
realistic data.
Variational Autoencoders (VAEs): VAEs encode input data into a latent
space and then decode it back to the original data format. They introduce
variability in the encoding, which helps generate new data points by sampling
from the latent space. Gen AI Training in Hyderabad
Autoregressive Models: These models generate data point by
point, considering the probability of each point given the previous ones.
Examples include models like GPT for text generation, which predict the next
word in a sentence based on the preceding words.
Diffusion Models: These
models generate data by iteratively refining noisy data. Starting from random
noise, they progressively enhance the data to resemble the training examples. DataScience Course in Hyderabad
Generative
AI is revolutionizing industries by enabling the creation of synthetic
data, enhancing creative processes, and driving innovations in fields like art,
music, and language processing. Understanding its principles and the diversity
of generative models is crucial for leveraging its potential effectively.
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