Self-Attention Mechanisms Courses and Certifications

Yannic Kilcher

Axial-DeepLab - Stand-Alone Axial-Attention for Panoptic Segmentation

Explore a novel approach to image processing using axial-attention, combining positional embeddings and factorized self-attention to achieve state-of-the-art results in image segmentation tasks.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Discover AI

The Inner Workings of Large Language Models - Visualizing Self-Attention Mechanisms

Dive into the mechanics of Large Language Models, exploring self-attention mechanisms, architectural designs, and performance optimization through clear visualizations and practical examples.

• YouTube
• 35 minutes
• On-Demand
• Free Video

Intermediate ChatGPT

22 ratings at DataCamp

Learn the architecture behind GPT models and master advanced prompt crafting to unlock ChatGPT's full potential.

• DataCamp
• 1 hour
• On-Demand
• Free Trial Available

Transformer Models and BERT Model

125 ratings at Coursera

Explore Transformer architecture and BERT model, learning key components like self-attention and applications in text classification, question answering, and natural language inference.

• Coursera
• 51 minutes
• On-Demand
• Paid Course

Coursera

Generative AI and Large Language Models

Discover generative AI fundamentals, from GANs to transformers, with hands-on experience using GPT, Hugging Face, and diffusion models for real-world applications.

• Coursera
• 1 day 3 hours 16 minutes
• On-Demand
• Paid Course

Packt

Foundations of AI, LLMs, and Development Environments

Master AI fundamentals, LLMs, and development environments through hands-on Python programming, transformer architecture, and OpenAI API integration in this comprehensive 12-hour journey.

• Coursera
• 12 hours 37 minutes
• On-Demand
• Paid Course

Transformer Models and BERT Model

1 review

Explore Transformer architecture and BERT model, learning key components like self-attention and applications in text classification, question answering, and natural language inference.

• Google Skills
• 30 minutes
• On-Demand
• Free Certificate

Stanford Seminar 2022 - Self Attention and Non-Parametric Transformers

Explore self-attention and Non-Parametric Transformers with PhD students discussing Transformer origins, intuitions, and recent advancements in neural network architecture.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Learning the Structure of EHR with Graph Convolutional Transformer - Edward Choi

Explore graph convolutional transformers for learning EHR structure, combining graph convolution and self-attention for supervised prediction tasks in healthcare analytics and representation learning.

• YouTube
• 1 hour 30 minutes
• On-Demand
• Free Video

Jay Alammar

The Narrated Transformer Language Model

Visual introduction to the Transformer architecture, explaining its components and applications in AI/ML. Accessible to various experience levels, covering tokenization, embedding, and output projection.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Code With Aarohi

Transformers for Computer Vision

Explore how Transformer models revolutionize computer vision tasks through Vision Transformers, image classification, object detection, and hybrid architectures with hands-on PyTorch implementations.

• YouTube
• 3 hours 55 minutes
• On-Demand
• Free Video

Yannic Kilcher

Linformer - Self-Attention with Linear Complexity

Detailed explanation of the Linformer model, which reduces self-attention complexity in transformers from O(n²) to O(n), improving efficiency while maintaining performance in NLP tasks.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Yannic Kilcher

Synthesizer - Rethinking Self-Attention in Transformer Models

Explores alternative attention mechanisms in Transformer models, proposing the Synthesizer model that learns synthetic attention weights without token-token interactions, showing competitive performance across various NLP tasks.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Alfredo Canziani

Self - Cross, Hard - Soft Attention and the Transformer

Explore attention mechanisms and Transformer architecture, covering self/cross and hard/soft attention, key-value stores, and practical implementation in PyTorch for advanced deep learning applications.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Collective Intelligence for Deep Learning - A Survey of Recent Developments

Survey of recent developments in collective intelligence for deep learning, exploring self-organization, emergent behavior, and cellular systems to address robustness and adaptability issues in neural networks.

• YouTube
• 28 minutes
• On-Demand
• Free Video