YoVDO

Transformer Models: Understanding Their Architecture and Functionality - Part 3

Offered By: Serrano.Academy via YouTube

Tags

Transformer Models Courses Machine Learning Courses Neural Networks Courses Attention Mechanisms Courses Embeddings Courses Text Generation Courses Positional Encoding Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Dive deep into the world of Transformer models in this comprehensive 44-minute video, the final installment of a three-part series. Explore the inner workings of these powerful machine learning models through visuals and friendly examples. Learn about key concepts such as tokenization, embeddings, positional encoding, attention mechanisms, and softmax. Understand how Transformers generate text one word at a time, perform sentiment analysis, and utilize neural networks. Discover the architecture of Transformer models and the process of fine-tuning. Perfect for those seeking to demystify this crucial technology in natural language processing and machine learning.

Syllabus

Introduction
What is a transformer?
Generating one word at a time
Sentiment Analysis
Neural Networks
Tokenization
Embeddings
Positional encoding
Attention
Softmax
Architecture of a Transformer
Fine-tuning
Conclusion


Taught by

Serrano.Academy

Related Courses

NeRF - Representing Scenes as Neural Radiance Fields for View Synthesis
Yannic Kilcher via YouTube
Perceiver - General Perception with Iterative Attention
Yannic Kilcher via YouTube
LambdaNetworks- Modeling Long-Range Interactions Without Attention
Yannic Kilcher via YouTube
Attention Is All You Need - Transformer Paper Explained
Aleksa Gordić - The AI Epiphany via YouTube
NeRFs- Neural Radiance Fields - Paper Explained
Aladdin Persson via YouTube