Applied AI Study Group #9

Join us for the Applied AI Study Group #9 this year – in person!

If you are eager to learn how Artificial Intelligence applies to real-world scenarios, understand its business implications, explore models in computer vision and natural language processing, gain hands-on experience, and connect with an AI-enthusiast community, you're in the right place!

Applied AI Study Group is designed to offer a hands-on learning environment for AI enthusiasts who thrive on building & deploying and maintaining Machine Learning models from scratch.

Starting from February 22, we will meet at our space in Taksim on Saturdays for 7 weeks. With a class of 15 people, this batch will be more interactive and fun! Moreover, we will have 5 online lab sessions on weekdays to get our hands dirty.

We will learn the skills needed to apply AI models in real-world scenarios, which will allow us to demonstrate our work and its practical applications, and discuss the most up-to-date arguments.

Watch our webinar for the 7th batch to learn more about us!

APPLICATION PERIOD

The application period consists of two parts;

1- First, you need to fill out the application form until February 7: APPLY

We will announce the first stage results on February 14, until 23:59.

2- The second stage will be an online interview, which may include follow-up questions based on your responses to the technical questions. You will receive a detailed email about the interview after you pass the first stage.

We will inform you about the second stage results by February 19, until 23.59

The first session will be on February 22, 11 am

Note: BEV Foundation (inzva) and Google Developers reserve the right to change or modify any of the conduct, design, and rules of the program at any time and at BEV Foundation’s sole discretion.

apply

COMMUNITY FEEDBACK

“The program covers a wide range of topics, from classical machine learning concepts to NLP (Natural Language Processing) and computer vision. It not only provides theoretical knowledge and coding skills but also offers the opportunity to hear firsthand how these concepts manifest in real-world scenarios.

Moreover, one of the most valuable aspects of the program is the chance to connect with people who share similar interests and concerns.

I believe that being part of such a community in such a vast industry is truly invaluable. ”

Discover the previous Applied AI Study Group's journey in our report.

Note: BEV Foundation (inzva) reserves the right to change or modify any of the conduct, design, and rules of the program at any time and in their sole discretion.

SCHEDULE:

Kick-off

Week 1: February 22

We will officially kick off the Applied AI Study Group today with a meeting to discuss our individual goals and collectively define our learning objectives.

Machine Learning in Business

Week 2: March 1, March 5

Week 3: March 8, March 12

In these weeks, we explore how Machine Learning can bring value to businesses. Throughout the section, we will discuss various topics related to ML engineering and its practical applications in the industry.

Natural Language Processing

Week 4: March 15, March 20

Week5: March 22, March 27

We will focus on the recent research and practical developments in Natural Language Processing (NLP) spanning the years from 2015 to 2024. Furthermore, we will explore other relevant topics including distillation and quantization for deployment, ethical considerations about large language models (LLMs), dialog models, efficient transformers, and language models for search, among others.

Advanced Computer Vision

Week 6: April 5, April 10

**Enes Sadi Uysal**
Computer Vision Guide

**Berkin Deniz Kahya**
Lab Session Guide

In this week, we will focus on advanced computer vision topics, including diffusion models, visual transformers, multimodal approaches, and practical advice. Through notebook sessions and demos, this part aims to provide a comprehensive understanding of advanced computer vision techniques.

Models in Action: Discussion on Specific Use Cases & Wrap-up

Week 7: April 12

This week, we’ll explore how the models we’ve covered throughout the course can be applied to diverse fields such as bioinformatics, drug design, and fintech, as well as how AI is utilized across various industries. Our community members will join us to discuss their respective domains, share how they apply machine learning to their projects, and talk about their journeys.

Lastly, we will have our graduation ceremony!

Curious about what each session will cover? Explore the detailed syllabus here.

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FREQUENTLY ASKED QUESTIONS

Please check out the Frequently Asked Questions here.

For further questions, you can reach us at ai@inzva.com

OUR SPACE

PROGRAM OUTCOMES

Learn from more experienced guides and peers in the field of computer science,
Acquire a state-of-the-art, domain-specific training,
Learn about recent models that have changed our daily lives like LLMs,
Hold discussions about different ways to approach a problem,
Become familiar with the ML in Business and the use cases,
Meet like-minded students,
Experience peer-to-peer learning with our guides,
Certificate of participation issued by inzva.

EXPECTATIONS

Successfully answering the technical question during your application,
Attending all classes (this is mandatory in order to graduate),
Completing the weekly assignments and delivering them by the deadline,
Being an active participant who is an enthusiast of all things AI-related,
Following the rules of our community: Code of Conduct

Please note that failure to meet these requirements without a good reason will result in your disqualification from the program and future inzva AI programs.

TECHNICAL REQUIREMENTS

Programming experience: The course is taught in Python. We assume you have basic programming skills (understanding of for loops, if/else statements, and data structures such as lists and dictionaries).
Mathematics: Basic linear algebra (matrix-vector operations and notation)
Machine Learning: Conceptual knowledge of machine learning (supervised learning, unsupervised learning, test data, validation, and some machine learning algorithms such as linear regression, and logistic regression)
Deep Learning: Theoretical knowledge is required on the deep learning concepts. The objective of this study group is to give the participants practical knowledge and hands-on programming experience with deep learning algorithms and frameworks. Theoretical aspects of deep learning algorithms will be only quickly summarized in the study group. Therefore, it may be hard for you to jump directly into the practical aspects if you do not have any background knowledge. Having attended inzva Deep Learning Study Group before is a big plus.

Apply

All participants have to abide by our CODE OF CONDUCT and LETTER OF CONSENT.

A BEV Foundation project inzva is a non-profit hacker community organizing study and project groups as well as camps in the fields of AI and Algorithm; and gathering CS students, academics, and professionals in Turkey.

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