Deep Learning на пальцах (DL Course AI)
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About
Deep Learning на пальцах is an introductory educational program designed to teach the fundamentals of deep learning to a Russian-speaking audience. The course is structured to take students from zero knowledge of neural networks or machine learning to a functional understanding of modern AI architectures. It serves as both an open online resource and a formal curriculum for graduate students at Novosibirsk State University and the Computer Science Center, ensuring high academic standards while remaining accessible to self-taught learners from any background. The curriculum is delivered through a blend of theoretical video lectures streamed on YouTube and hands-on practical assignments. Students explore a wide range of topics, including computer vision, natural language processing, speech recognition, and reinforcement learning. Practical work is conducted in Python, utilizing popular tools like NumPy, Jupyter Notebooks, and PyTorch. Assignments are designed to build intuition, starting from manual implementations of algorithms like K-nearest neighbor and linear classifiers before moving to complex tasks like hotdog recognition via transfer learning or training LSTMs for part-of-speech tagging. This course is ideal for software developers, students, and data science enthusiasts who prefer learning in Russian. Since it requires no prior background in machine learning, it is particularly well-suited for those transitioning from traditional programming or related technical fields. The inclusion of community support through platforms like ODS.ai and ClosedCircles provides a collaborative environment where learners can discuss challenges and receive help from industry specialists and peers throughout their learning journey. What distinguishes this program is its balance between academic rigor and practical accessibility. Unlike many theoretical courses, it encourages students to implement key components by hand to understand the underlying mathematics before relying on high-level frameworks like PyTorch. Furthermore, the course features guest lectures from industry experts in niche fields like audio processing and object detection, providing insights that go beyond standard textbook examples. Its open-source nature on GitHub allows anyone to follow the curriculum at their own pace without financial barriers.
Pros & Cons
Requires no prior machine learning or neural network knowledge
Includes hands-on implementation of algorithms from scratch to build intuition
Provides free access to all slides and video materials without registration
Covers advanced topics like Transformers, GANs, and Reinforcement Learning
Supported by a large and active Russian-speaking ML community
All instructional content and materials are in Russian only
Course materials have not been significantly updated since 2019
No automated grading system available for independent learners
Lacks a formal certification path for non-university students
Use Cases
Software developers can transition into AI roles by following the structured path from basic Python to complex PyTorch models.
Russian-speaking students can supplement their university studies with high-quality, practical deep learning lectures and assignments.
Self-taught learners can build a portfolio by completing the seven GitHub-hosted assignments covering CNNs, RNNs, and NLP.
Data science hobbyists can learn the internal mechanics of neural networks by implementing backpropagation and linear classifiers manually.
Platform
Features
• google colab integration
• python-based practical assignments
• youtube video lectures
• community support via ods.ai
• computer vision and segmentation
• reinforcement learning intro
• nlp and word2vec modules
• pytorch framework training
FAQs
Do I need prior knowledge of Machine Learning to start this course?
No, the course is designed specifically for beginners and does not require any background in neural networks or machine learning. It begins with the basics of Python, NumPy, and general ML concepts.
What programming languages and frameworks are used in the assignments?
The course primarily uses Python for all practical work. Students will utilize NumPy for basic mathematical operations and PyTorch for building and training complex neural network architectures.
Where can I get help if I get stuck on an assignment?
Learners are encouraged to join the ODS.ai community or the Telegram group linked on the website. These platforms provide active discussion spaces where creators and peers offer assistance.
What specific topics are covered in the curriculum?
The course covers a broad spectrum including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), NLP, Reinforcement Learning, and Generative Adversarial Networks (GANs).
Is there a certificate provided upon completion?
The website does not mention a formal certificate for independent online participants. However, it is taught as a formal credit-bearing course for students at NSU and the CS Center.
Pricing Plans
Free
Free Plan• 15 Video Lectures
• 7 Practical Assignments
• Downloadable Lecture Slides
• PyTorch Framework Training
• Community Discussion Access
• GitHub Repository Access
• Google Colab Integration
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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