Self-Attention Courses and Certifications

StatQuest with Josh Starmer

Transformer Neural Networks, ChatGPT's Foundation, Clearly Explained

Clear explanation of Transformer Neural Networks, the foundation of ChatGPT and modern AI. Covers key concepts like word embedding, self-attention, and encoder-decoder architecture.

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

Simons Institute

The Emergence of Clusters in Self-Attention Dynamics

Explore the formation of clusters in self-attention dynamics, examining their impact on transformer models and computational processes.

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

Generative AI Language Modeling with Transformers

18 ratings at Coursera

Learn to apply transformer-based models for NLP tasks, covering encoder-decoder architectures, attention mechanisms, and implementations of BERT and GPT models using PyTorch for text classification and translation.

• Coursera
• 8 hours 48 minutes
• On-Demand
• Free Online Course (Audit)

Open Data Science

Vision Transformer and Its Applications

1 review

Explore vision transformers' architecture, self-attention in computer vision, and applications in recognition, segmentation, and multi-modal learning. Discover their potential for general-purpose model architectures.

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

Generative AI: Working with Large Language Models

1798 ratings at LinkedIn Learning

Explore a user-friendly approach to working with transformers and large language models for natural language processing.

• LinkedIn Learning
• 1 hour 9 minutes
• On-Demand
• Free Trial Available

Generative AI: Introduction to Large Language Models

850 ratings at LinkedIn Learning

Gain a foundational knowledge of how large language models and other Generative AI models work.

• LinkedIn Learning
• 1 hour 36 minutes
• On-Demand
• Free Trial Available

Rethinking and Improving Relative Position Encoding for Vision Transformer - Lecture 23

Explore vision transformer improvements through relative position encoding, examining bias modes, piecewise indexing, and 2D calculations for enhanced performance in image processing tasks.

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

Transformers: Text Classification for NLP Using BERT

456 ratings at LinkedIn Learning

Learn about transformers, the go-to architecture for NLP and computer vision tasks.

• LinkedIn Learning
• 55 minutes
• On-Demand
• Free Trial Available

TensorFlow: Working with NLP

169 ratings at LinkedIn Learning

Learn about using transformers in natural language processing with TensorFlow.

• LinkedIn Learning
• 41 minutes
• On-Demand
• Free Trial Available

CodeEmporium

Deep Dive into the Transformer Encoder Architecture

Detailed exploration of transformer encoder architecture, covering embeddings, positional encodings, self-attention mechanisms, and key components for effective natural language processing.

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

Yannic Kilcher

TransGAN - Two Transformers Can Make One Strong GAN - Machine Learning Research Paper Explained

Explore TransGAN, a novel approach using transformers for image generation. Learn about its architecture, key techniques, and competitive performance in various datasets.

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

Yannic Kilcher

Nyströmformer- A Nyström-Based Algorithm for Approximating Self-Attention

Explore Nyströmformer, a novel algorithm for approximating self-attention in Transformers with linear complexity, enabling processing of longer sequences and improving scalability in natural language tasks.

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

tinyML

On-Device Speech Models Optimization and Deployment for Mobile Hardware

Explore on-device speech model optimization and deployment, covering streaming-aware design, quantization techniques, and benchmarks for popular speech processing model topologies on mobile platforms.

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

Institute for Advanced Study

Emergent Linguistic Structure in Deep Contextual Neural Word Representations - Chris Manning

Explore emergent linguistic structures in deep neural word representations with Stanford's Chris Manning, examining language models, attention mechanisms, and vector space analysis.

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

CodeEmporium

Transformers - From Zero to Hero

Comprehensive deep dive into transformer architecture, covering key concepts from self-attention to tokenization. Includes hands-on coding for both encoder and decoder implementations.

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