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#Self-Attention Mechanisms

Showing: 12 courses
Stanford University

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

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Explore graph convolutional transformers for learning EHR structure, combining graph convolution and self-attention for supervised prediction tasks in healthcare analytics and representation learning.
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15 Lesons
1 hour 30 minutes
On-Demand
Free-Video
Stanford University

Stanford Seminar 2022 - Self Attention and Non-Parametric Transformers

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Explore self-attention and Non-Parametric Transformers with PhD students discussing Transformer origins, intuitions, and recent advancements in neural network architecture.
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1 Lesons
1 hour 6 minutes
On-Demand
Free-Video
USENIX

Models on the Move: Towards Feasible Embedded AI for Intrusion Detection on Vehicular CAN Bus

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Innovative AI model for efficient intrusion detection on vehicular CAN bus, enhancing automotive cybersecurity with high accuracy and low latency using FPGA hardware acceleration.
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1 Lesons
18 minutes
On-Demand
Free-Video
Discover AI

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

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Dive into the mechanics of Large Language Models, exploring self-attention mechanisms, architectural designs, and performance optimization through clear visualizations and practical examples.
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1 Lesons
35 minutes
On-Demand
Free-Video
Yannic Kilcher

Synthesizer - Rethinking Self-Attention in Transformer Models

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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.
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14 Lesons
48 minutes
On-Demand
Free-Video
Yannic Kilcher

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

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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.
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9 Lesons
56 minutes
On-Demand
Free-Video
Alfredo Canziani

Self - Cross, Hard - Soft Attention and the Transformer

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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.
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16 Lesons
1 hour 12 minutes
On-Demand
Free-Video
Simons Institute

Knowledge Is Embedded in Language Neural Networks but Can They Reason?

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Explore language models' reasoning capabilities, from n-grams to transformers, and discuss future research directions in neural state machines for improved natural language understanding.
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16 Lesons
50 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Collective Intelligence for Deep Learning - A Survey of Recent Developments

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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.
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10 Lesons
28 minutes
On-Demand
Free-Video
Envato Tuts+

Transformers: Introduction and Fundamentals - Day 1

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Explore the fundamentals of Transformers, a powerful deep learning architecture revolutionizing natural language processing and beyond.
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1 Lesons
44 minutes
On-Demand
Free-Video
Yannic Kilcher

Linformer - Self-Attention with Linear Complexity

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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.
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13 Lesons
50 minutes
On-Demand
Free-Video
Jay Alammar

The Narrated Transformer Language Model

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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.
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9 Lesons
30 minutes
On-Demand
Free-Video