🚀 Dive into the World of Generative Modelling! Discover how machines not only interpret but create data. From synthesizing new images to crafting complex sounds, explore the magic behind machine learning's ability to generate new content. Perfect for both beginners and seasoned pros looking to deepen their understanding of AI's creative power. 🌐 #MachineLearning #GenerativeModels #AIInnovation #TechTalk

1. 100 Concepts
of AI
Generative Modelling
Anupama Kate, Data scientist | SlideShare

2. Understanding Generative
Modeling in Machine
Learning
Explore the fascinating world of generative modeling, a powerful
technique in machine learning that enables computers to learn and
generate new data. Discover how these models can create realistic
images, text, and other content, revolutionizing fields from art to natural
language processing.

3. Introduction to Generative
Modeling
1. Generative modeling is a machine learning approach focused on
learning the underlying distribution of data. This allows the model
to generate new, realistic samples that resemble the training data.
2. In contrast, discriminative models are trained to directly map inputs
to outputs, without learning the full data distribution.
3. Generative models are valuable in machine learning as they can
be used for tasks like data augmentation, anomaly detection, and
semi-supervised learning - situations where generating realistic
samples is beneficial.

4. Key Characteristics of Generative
Models
Explains Joint Probability
Distribution
Generative models learn to capture
the underlying joint probability
distribution P(X, Y) of the data,
allowing them to generate new
samples that resemble the original
data.
Ability to Generate New Data
A key strength of generative models
is their capacity to create novel data
instances that are statistically similar
to the training data, expanding the
available dataset without requiring
additional labeling.
Application in Low-Data Scenarios
Generative models can be particularly useful in situations where data is scarce, as
they can generate synthetic samples to augment the training set and improve model
performance.

5. Examples of Generative Models
Naive Bayes
A simple
probabilistic
classifier based on
applying Bayes'
theorem. Effective
for text classification
tasks.
Hidden Markov
Model (HMM)
A statistical model
used to represent a
Markov process with
unobserved (hidden)
states. Useful for
speech recognition
and natural
language
processing.
Gaussian
Mixture Models
(GMM)
A probabilistic model
that assumes all the
data points are
generated from a
mixture of a finite
number of Gaussian
distributions.
Generative
Adversarial
Networks
(GANs)
A deep learning
framework that pits
two neural networks
against each other -
a generator and a
discriminator.
Powerful for
generating realistic
synthetic data.

6. Applications of Generative Models
Image and Video
Generation
Generative models excel at
creating photorealistic
images and videos by
learning the underlying
patterns and distributions in
visual data. They can
produce novel, high-quality
content for entertainment,
design, and artistic
Speech Synthesis
Generative models are used
to generate natural-sounding
human speech, enabling
text-to-speech systems and
voice assistants. They can
capture the complex
relationships between text,
audio, and prosody to
produce highly realistic and
expressive speech.
Drug Discovery
Generative models can
explore vast chemical
spaces to identify promising
drug candidates. They learn
the patterns in molecular
structures and properties,
and can generate novel
molecular designs with
desired therapeutic
characteristics, accelerating

7. Advantages of Generative Models
Generating
New Data
Generative models
can create new,
realistic data
samples that
closely match the
statistical
properties of the
original training
data. This is
valuable for
augmenting
datasets,
especially in
domains where
data is scarce.
Semi-
Supervised
Learning
Generative models
can be leveraged
in semi-supervised
learning, where
they learn the
underlying data
distribution from
both labeled and
unlabeled
examples. This
allows them to
make better use of
limited labeled
data.
Capturing
Data
Richness
Generative models
can model the
complex, high-
dimensional
distributions of
real-world data like
images, audio, and
text. This allows
them to capture
the full richness
and nuance of the
data, going
beyond simple
discriminative
patterns.
Creativity and
Exploration
By learning the
data distribution,
generative models
can be used to
explore and
discover new,
creative
possibilities within
the data manifold,
opening up novel
applications and
use cases.

8. Challenges with Generative
Models
Generative models can be highly complex, often requiring substantial
computational resources to train and run effectively. Additionally, these
models demand careful tuning and optimization of various
hyperparameters to achieve optimal performance.
Striking the right balance between model complexity and practical
feasibility is a key challenge in deploying generative models in real-world
applications.

9. Future of Generative Modeling
1 Advancing Architectures
Researchers are developing more sophisticated generative model architectures,
such as Variational Autoencoders and Transformer-based models, to capture even
richer data distributions and generate higher-fidelity outputs.
2 Bridging the Gap
As generative models become more robust and versatile, they will play a crucial
role in bridging the gap between human and machine intelligence, enabling more
natural and intuitive human-computer interactions.
3 Societal Impacts
The proliferation of generative models will have far-reaching societal impacts, from
revolutionizing creative industries to automating content creation and posing
challenges in areas like fake media detection and copyright protection.

10. Conclusion
In conclusion, generative models have emerged as a powerful tool in the field of machine
learning, with the ability to generate new data samples that capture the richness and
complexity of real-world distributions. These models have significant applications in areas
such as image and video generation, speech synthesis, and even drug discovery,
showcasing their versatility and potential to drive innovation.
As we look to the future, the continued advancement and refinement of generative modeling
techniques hold the promise of even greater breakthroughs. We encourage you to further
explore this exciting field, delving deeper into the latest research and developments, and
consider how you might harness the power of generative models to tackle the challenges
and opportunities in your own work or studies.

11. Q&A
We've covered a lot of ground on the fascinating topic of generative
modeling. Now is your chance to dive deeper and ask any lingering
questions. Our team of experts is here to provide thorough and insightful
responses to help further your understanding.