Applied Deep Learning: From Neural Networks to GenAI

Gain an in-depth understanding of deep learning — its principles and applications in NLP, computer vision, and agent creation.

Apply theoretical concepts to real-world challenges: build and fine-tune neural networks while following industry best practices.

Best fit

Who is this program ideal for?

Data Scientists looking to enhance their skill sets by incorporating deep learning into their analytical workflows.

AI Developers seeking a deeper understanding of generative models and DL theory.

Students and Researchers in AI, computer science, or related fields who need a strong foundation in deep learning.

About the course

Explore the fast-growing field of deep learning

The curriculum covers foundational concepts and techniques, starting with the basics of neural networks and progressing to advanced architectures, including convolutional and recurrent networks, backpropagation, regular and variational autoencoders, and embeddings. Students will also explore the design and development of intelligent agents using deep learning principles.

By the end of the course, students will have a solid theoretical foundation in deep learning and practical experience applying these concepts to real-world challenges.

15 weeks

Scope

Tuesdays 17:00-21:00
Fridays 09:00-13:30

+ 20 hours of hands-on practice per week

Time

Hybrid, in-person at Tel Aviv University campus. Frontal lectures + Zoom streaming and recordings

Format

Advantages

Why join us?

Understand deep learning principles and applications in NLP, computer vision, and agents.ding

Build and fine-tune neural networks following industry standards.”

Learn core concepts like perceptrons, convolutional and recurrent networks.

Master advanced architectures, including variational autoencoders and embeddings.

Apply deep learning methods to solve real-world challenges.

Acquire skills relevant to industry demands in this fast-growing field.

our team

Course lecturers

Inbar Huberman

PhD from The Hebrew University of Jerusalem

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Lecturer at Deep Learning course

Karin Brisker

Data Scientist at Microsoft Israel

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Lecturer at ML foundations

Omri Allouche

Head of Research at Gong.io, Data Scientist and Lecturer

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Lecturer at Deep Learning course

course plan

Syllabus

Our course combines theory, case studies, and hands-on workshops to put your learning into practice.

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Customization

Elective Courses

Customize your learning: from Week 10, pick an elective that matches your goals

Explainable AI

How can we make AI models more interpretable and trustworthy? Learn to use Captum, BERTViz, and InterpretML, explore methods like LIME and SHAP, and discover key concepts in explainability, ethical AI, and bias mitigation — all while working with both structured and unstructured data.

Explore full syllabus

End-to-End Agent Deployment

Use DSPy, a powerful framework for programming language models, to build modular AI systems. Optimize prompts and weights, and develop models for classification, retrieval-augmented generation (RAG) pipelines, and agent-based workflows.

Syllabus coming soon

feedback

Our Alumni

Andrey Nikitin

Data Science Manager, Cyera

The course is great, I think it's the best professional course I have taken and for me personally, it's a good substitution for a master's degree (for now). Even though I'm already working as a Data Scientist i still learn new things, there are always fields that I'm less proficient in and the course fills the gap.

Liad Yosef

Principal Software Engineer, Shopify

You know they say go with your passion, right? I've been programming since I was a kid, but I never really dealt with Data Science or Machine Learning before Y-Data. I already knew the math part of the introductory courses but they were so fast-paced that I wasn't bored and quickly enough we got into supervised learning and deep learning. This gave me the tools to do things that I couldn't have done before, and let me explore and widen the area of my thoughts.

Lior Tabori

Senior Data Scientist, Stampli

I wanted to get into the world of data and data science. I had a feeling that this field is mine. That was my main purpose, to get the most out of this program and out of the industry project. I think our learning group was most important in my experience. It was small but diverse. Everyone is a specialist in something a little bit different so we really helped each other. There are very good students in this program.

Rachel Shalom

Principal Data Scientist, Dell Technologies

I realized that as a product manager in a travel tech startup, I needed heavy tools to analyze data, do predictions and more. So I started checking all kinds of data science boot camps, and machine learning academies, but unlike most of them, Y-DATA looked realistic. I chose Y-DATA because one year is better in terms of understanding things. Also, I could combine it with my previous work.

Yechiel Levy

CTO at OptimalQ

In a young startup like the one I own, we are doing a bit of everything, from big data to DevOps to data science. As we grow bigger. algorithms get more complicated. I joined Y-DATA to understand my data team better. Now I can understand their work better, know how they're approaching the problem. It helps us move along much faster and bridges the gap between management, engineering and data science teams.

Ido Nissim

Data Engineer at AllCloud

I think the very best thing about the course is the people. The selection of the students for the course was really good. Heterogeneous people from all kinds of fields and different backgrounds - that's really good. We had some projects together, and worked as groups, which was a good way to get to know other people. We were all sitting in the classroom together, talking and trying to figure out how to do the homework later on. It's great.

Amit Alon

Data Scientist at KHealth

I was looking for the best place to get ML Background, to learn more techniques, better and wider knowledge, especially in deep learning, which I didn’t know everything about. I chose Y-Data because it was presented as a program that can mediate the gap between academia and industry. This was exactly what I was looking for. I don’t have professional experience in ML but Y-Data gave me a really good background so I can bring a lot to the table in addition to my research background.

Arseny Levin

Fraud Detection Lead at DoubleVerify

Great experience so far! Personally, for me, the course exceeded my expectations. I usually stay away from courses since I'm a self learner. Courses usually spend too much time on the unimportant parts (too much history, too much theory, repetitive exercises etc.)

However, during Y-DATA courses we had exactly the right balance of practice and theory.

Nir Aviv

Software Engineer and Data Scientist at Fiverr

For me, the most important aspect of the program is the industry project. There's nothing like working on a real problem with experts in the field. I feel that the classes prepared me well for this kind of hands-on data science work. In particular, the variety of lecturers from tech and academia is definitely an advantage of the program.

Jonathan Ohnona

Data Scientist at eToro

I'm an Engineer. I studied math and physics, and financial engineering. I choose Y-DATA because I wanted a better understanding of the algorithms. When you have access to machine learning techniques, you have access to more tools, allowing you to do more things. For instance, in my field, in time-series analysis, you want to better predict and better focus. Studying in Y-DATA is like building a muscle. You need to work on a muscle to be a better, stronger person. It's a very good program because it shows many things.

Tal Ben-Yehuda Heletz

Deep Learning Researches at Trigo

It was obvious to me that math is the field for me. I did my B.Sc and M.Sc in math. In the industry, you can do a lot with math, but you must have knowledge in computer science as well.

Y-Data was exactly right for me - it let me combine my background with computer science and strong data science foundations.

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