Attention Mechanisms Courses and Certifications

Attention Mechanism with Google Cloud

Discover how neural networks focus on key inputs using attention mechanisms to enhance translation, summarization, and question answering tasks.

• Udacity
• 1 hour
• On-Demand
• Free Online Course

Serrano.Academy

The Math Behind Attention Mechanisms

Demystify attention mechanisms in Transformer models through visuals, examples, and mathematical explanations, focusing on embeddings, similarity, and key-query-value matrices.

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

Serrano.Academy

Keys, Queries, and Values: Understanding Attention Mechanisms in Language Models

Dive into the mechanics of attention in Large Language Models through a unique astronomical analogy, exploring how word embeddings and similarity create the fabric of language understanding.

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

Serrano.Academy

Celestial Mechanics of Attention Mechanisms

Explore the intricate workings of attention mechanisms in machine learning through celestial analogies and engaging explanations in this comprehensive presentation.

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

Donato Capitella

Adding Attention Mechanisms to Language Translation RNNs in PyTorch - Lab 4.8

Master PyTorch implementation of attention mechanisms in neural machine translation, from theory to practice. Build and visualize a Bahdanau-style attention model for enhanced translation performance.

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

MLOps World: Machine Learning in Production

The Celestial Mechanics of Attention Mechanisms

Discover how transformers work through a captivating space analogy, exploring embeddings and attention mechanisms as gravitational forces between celestial objects.

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

UofU Data Science

Machine Translation: BLEU Score, Decoding Methods, and Attention Mechanisms

Dive into essential machine translation concepts, exploring BLEU scoring metrics, decoding techniques, and attention mechanisms for natural language processing.

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

Sequence Models & The Dawn of Attention

Discover why RNNs and LSTMs fail with long sequences, then master attention mechanisms by building QKV paradigms and reusable PyTorch modules from scratch.

• CodeSignal
• 3 hours
• On-Demand
• Free Certificate

Attention Mechanisms and Transformer Models Course

Master attention mechanisms and transformer architectures powering modern GenAI systems like GPT and BERT through hands-on implementation and real-world demos.

• Coursera
• 4 hours 29 minutes
• On-Demand
• Paid Course

Attention Mechanism

Learn about the attention mechanism in neural networks, its applications in machine learning tasks, and how it enhances performance in various AI applications.

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

Yannic Kilcher

Attention Is All You Need

Explore the groundbreaking Transformer architecture, revolutionizing sequence transduction with attention mechanisms. Learn its superior performance in machine translation and generalization to other NLP tasks.

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

Pascal Poupart

CS480-680 Lecture 19 - Attention and Transformer Networks

Explore attention mechanisms, transformer networks, and their applications in machine learning, including multihead attention and normalization techniques.

• YouTube
• 1 hour 23 minutes
• On-Demand
• Free Video

Reducing Hallucinations in LLMs via Decoding, Detection, and Citation

Explore three complementary approaches to reduce LLM hallucinations: DoLa for improved truthfulness, Lookback Lens for hallucination detection, and SelfCite for generating fine-grained citations.

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

Sequence Models

31231 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 7 minutes
• On-Demand
• Paid Course

Yannic Kilcher

Train Short, Test Long - Attention With Linear Biases Enables Input Length Extrapolation

ALiBi: A novel attention mechanism enabling transformers to process longer sequences than trained on. Uses linear biases instead of position encodings, improving efficiency and extrapolation capabilities.

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