Attention Mechanisms Courses and Certifications

Unify

SparQ Attention: Bandwidth-Efficient LLM Inference

Explore SparQ Attention, a technique for increasing LLM inference throughput by reducing memory bandwidth in attention blocks through selective history fetching, applicable to off-the-shelf models without retraining.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Unify

Attention with Linear Biases Explained

Explore attention mechanisms with linear biases in transformer models, enabling efficient extrapolation to longer sequences without explicit positional embeddings.

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

Discover AI

StreamingLLM: Enabling Infinite Sequence Length in Large Language Models

Dive into StreamingLLM's framework for extending LLM attention windows to handle infinite sequences, featuring practical implementation insights and code analysis.

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

Data Science Courses

Attention Mechanism and Self-Attention in Deep Learning - Lecture 9

Dive into attention mechanisms and self-attention concepts in deep learning, exploring their revolutionary impact on NLP and discovering practical applications in sequence models.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

UofU Data Science

Pairwise Feature Importance and Effective Attention in Self-Attention Networks

Dive into advanced concepts of attention mechanisms, exploring effective attention computation, norm-based analysis, and practical applications in neural networks.

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

Attention Mechanism - Italiano

Esplora il meccanismo di attenzione nelle reti neurali, una tecnica chiave per migliorare le prestazioni in traduzione automatica, riassunti e risposta alle domande.

• Coursera
• 19 minutes
• On-Demand
• Free Online Course (Audit)

Attention Mechanism - 한국어

이 과정에서는 신경망이 입력 시퀀스의 특정 부분에 집중할 수 있도록 하는 강력한 기술인 주목 메커니즘을 소개합니다. 주목 메커니즘의 작동 방식과 이 메커니즘을 다양한 머신러닝 작업(기계 번역, 텍스트 요약, 질문 답변 등)의 성능을 개선하는 데 활용하는 방법을 알아봅니다.

• Coursera
• 18 minutes
• On-Demand
• Free Online Course (Audit)

Attention Mechanism - 简体中文

本课程将向您介绍注意力机制，这是一种强大的技术，可令神经网络专注于输入序列的特定部分。您将了解注意力的工作原理，以及如何使用它来提高各种机器学习任务的性能，包括机器翻译、文本摘要和问题解答。

• Coursera
• 18 minutes
• On-Demand
• Free Online Course (Audit)

Dikkat Mekanizması

Keşfedin nöral ağlarda dikkat mekanizmasının temellerini ve bu güçlü tekniğin makine öğrenimi, metin özetleme ve soru yanıtlama görevlerindeki uygulamalarını.

• Coursera
• 18 minutes
• On-Demand
• Free Online Course (Audit)

How I Learned to Stop Worrying and Love the Transformer - Stanford CS25 Lecture

Explore the Transformer's evolution and future directions with Ashish Vaswani, co-author of "Attention is All You Need". Gain insights into motivations, developments, and promising research areas in AI and NLP.

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

Overview of Transformers - Stanford CS25: Lecture 1

Comprehensive introduction to NLP, Transformers, and AI agents, covering history, functionality, recent breakthroughs, applications, and challenges in the field of artificial intelligence.

• YouTube
• 1 hour 17 minutes
• On-Demand
• Free Video

Advanced Computer Vision and Deep Learning

1 review

Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.

• Udacity
• 2 weeks, 4-5 hours a week
• On-Demand
• Paid Course

Explainable Machine Learning (XAI)

Develop transparent and trustworthy AI solutions using explainable machine learning techniques. Implement local and global explainability methods, visualize neural networks, and explore emerging approaches for large language models.

• Coursera
• 14 hours 33 minutes
• On-Demand
• Free Online Course (Audit)

Sequence Models

30411 ratings at Coursera

Master sequence models for NLP tasks, including RNNs, LSTMs, and transformers. Apply these techniques to speech recognition, language modeling, and machine translation.

• Coursera
• 1 day 13 hours 17 minutes
• On-Demand
• Free Online Course (Audit)

Modern Natural Language Processing in Python

1709 ratings at Udemy

Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab

• Udemy
• 5 hours 48 minutes
• On-Demand
• Paid Course