This repository contains comprehensive educational materials for a Deep Learning course, including lecture notes, video playlists, and detailed syllabi covering a wide range of topics from foundational neural networks to advanced architectures and applications. It is primarily targeted at university students and researchers seeking a structured and in-depth understanding of deep learning concepts and methodologies.

How It Works

The course material is structured thematically, with each topic broken down into specific sub-concepts. The approach is to provide a theoretical foundation supported by practical examples and discussions of state-of-the-art architectures and techniques. Key areas covered include convolutional neural networks, recurrent neural networks, transformers, generative models (GANs, VAEs), and self-supervised learning, with a strong emphasis on understanding the underlying principles and evolution of these models.

Quick Start & Requirements

• Installation: No specific installation instructions are provided as this is an educational resource, not a software library.
• Prerequisites: A strong background in mathematics (linear algebra, calculus, probability) and programming (Python) is recommended. Familiarity with deep learning frameworks like PyTorch is beneficial for practical application.
• Resources: Access to YouTube for video lectures and GitHub for lecture notes and code examples.

Highlighted Details

• Extensive coverage of various neural network architectures, including detailed comparisons and historical context (e.g., LeNet, AlexNet, VGG, ResNet, EfficientNet, MobileNet, Transformers, BERT, GPT).
• In-depth exploration of generative models, including GANs, VAEs, and their numerous variants and applications.
• Detailed discussions on text and speech processing, including word embeddings, sequence-to-sequence models, attention mechanisms, and speech recognition techniques.
• Includes a dedicated section on PyTorch, covering its core functionalities for deep learning.

Maintenance & Community

The project is associated with Alexander Dyakonov and the Faculty of Computational Mathematics and Cybernetics at Moscow State University. The primary community interaction point is the provided YouTube playlist for lectures.

Licensing & Compatibility

The repository content is generally available for educational purposes. Specific licensing details for individual components or code snippets are not explicitly stated in the README.

Limitations & Caveats

The repository focuses on lecture materials and does not provide a runnable software framework. Practical implementation and experimentation would require users to set up their own deep learning environments and potentially adapt the provided code examples. Some lecture topics are marked as "without video" or "without slides," indicating incomplete content for those specific areas.