Our cohort-based educational activities are meant to be taken by a group of students simultaneously. Hence the name “cohort-based”.
It’s a hands-on, feedback-based experience where you can share your understanding and applications of the course material with your fellow students.
During our live sessions, you will be able to engage directly with our lecturers and ML practitioners from international tech companies and startups.
After you complete your course, you retain access to all the materials and our Slack community, and you can participate in our alum events to expand your network.
We offer weekly Paper Club meetings for everyone passionate about knowledge exchange.
During these meetings, you will learn about the latest research papers in ML and discuss the material with your peers.
ML Lead at Proxet
CPO & Co-founder at Reface
Head of Product at Pawa
Managing Partner at Pawa
PhD in CS, AI Lead at Infopulse
Kaggle Competition Master, R&D TeamLead at Respeecher
Senior ML Engineer at Energi
more engagement than traditional learning libraries
of staff feel more confident and effective
average uplift on key metrics for teams that attend
"AI? In my day, we called it Data and Statistics. And it wasn't sexy at all" – Marijn Markus, Managing Data Scientist, who has more than 6 years of experience in this industry. In the live talk, on November 25, at 12 AM, Kyiv time, Marijn Markus will share his views and experience on Data Science. The vast differences between theory and practice, the dysfunctionality of organizations, And how Data can be applied to change lives - from burnouts and fighting crime to whales and farming. From distinguishing science and fiction, creating friction and first introductions, to research, development, team management and implementing AI in production.
Let's talk about robots? They really fit well into human existence and help us solve many routine tasks. Such as home care, delivery of things over short distances, and this is just the beginning. But is the explicit mapping actually necessary for accurate indoor navigation?
Stable Diffusion is a text-to-image diffusion model capable of generating photorealistic images for any text input. This model provides an overview of all available model control points and derives from the concept of Latent Diffusion Models (LDM). By decomposing the image formation process into sequential applications of noise-reducing autoencoders, diffusion models (DM) achieve state-of-the-art results in synthesizing images and other data.
Get in touch with us to learn more about the project or share your idea on how we can become better for youContact