How does Transformer translate? #deeplearning #machinelearning

CodeEmporium · Beginner ·🧠 Large Language Models ·2y ago

Key Takeaways

The video explains how the Transformer model translates text using its encoder and decoder components, specifically focusing on the generation of embeddings and the sequential generation of translated words.

Full Transcript

the Transformer consists of two key components an encoder and a decoder the encoder takes the English words simultaneously and it generates embeddings for every word simultaneously these embeddings are vectors that encapsulate the meaning of the word similar words have closer numbers in their vectors the decoder takes these embeddings from the encoder and the previously generated words of the translated French sentence and then it uses them to generate the next French word and we keep generating the French translation one word at a time until the end of sentence is reached
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from CodeEmporium · CodeEmporium · 0 of 60

← Previous Next →
1 Linear Regression and Multiple Regression
Linear Regression and Multiple Regression
CodeEmporium
 2 Logistic Regression - THE MATH YOU SHOULD KNOW!
Logistic Regression - THE MATH YOU SHOULD KNOW!
CodeEmporium
 3 Generative Adversarial Networks - FUTURISTIC & FUN AI !
Generative Adversarial Networks - FUTURISTIC & FUN AI !
CodeEmporium
 4 Deep Learning on the Cloud - GPU TO LEARN FASTER
Deep Learning on the Cloud - GPU TO LEARN FASTER
CodeEmporium
 5 Deep Mind's AlphaGo Zero - EXPLAINED
Deep Mind's AlphaGo Zero - EXPLAINED
CodeEmporium
 6 Mask Region based Convolution Neural Networks - EXPLAINED!
Mask Region based Convolution Neural Networks - EXPLAINED!
CodeEmporium
 7 Attention in Neural Networks
Attention in Neural Networks
CodeEmporium
 8 Depthwise Separable Convolution - A FASTER CONVOLUTION!
Depthwise Separable Convolution - A FASTER CONVOLUTION!
CodeEmporium
 9 One Neural network learns EVERYTHING ?!
One Neural network learns EVERYTHING ?!
CodeEmporium
 10 Neural Voice Cloning
Neural Voice Cloning
CodeEmporium
 11 AI creates Image Classifiers…by DRAWING?
AI creates Image Classifiers…by DRAWING?
CodeEmporium
 12 Unpaired Image-Image Translation using CycleGANs
Unpaired Image-Image Translation using CycleGANs
CodeEmporium
 13 K-Means Clustering - EXPLAINED!
K-Means Clustering - EXPLAINED!
CodeEmporium
 14 Random Forest Classification
Random Forest Classification
CodeEmporium
 15 Data Science in Finance
Data Science in Finance
CodeEmporium
 16 Hypothesis testing with Applications in Data Science
Hypothesis testing with Applications in Data Science
CodeEmporium
 17 A/B Testing - Simply Explained
A/B Testing - Simply Explained
CodeEmporium
 18 The Kernel Trick - THE MATH YOU SHOULD KNOW!
The Kernel Trick - THE MATH YOU SHOULD KNOW!
CodeEmporium
 19 Support Vector Machines - THE MATH YOU SHOULD KNOW
Support Vector Machines - THE MATH YOU SHOULD KNOW
CodeEmporium
 20 Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
CodeEmporium
 21 History of Calculus - Animated
History of Calculus - Animated
CodeEmporium
 22 Curiosity in AI
Curiosity in AI
CodeEmporium
 23 DropBlock - A BETTER DROPOUT for Neural Networks
DropBlock - A BETTER DROPOUT for Neural Networks
CodeEmporium
 24 Autoencoders - EXPLAINED
Autoencoders - EXPLAINED
CodeEmporium
 25 Recurrent Neural Networks - EXPLAINED!
Recurrent Neural Networks - EXPLAINED!
CodeEmporium
 26 LSTM Networks - EXPLAINED!
LSTM Networks - EXPLAINED!
CodeEmporium
 27 Building an Image Captioner with Neural Networks
Building an Image Captioner with Neural Networks
CodeEmporium
 28 10 Machine Learning Questions - ANSWERED!
10 Machine Learning Questions - ANSWERED!
CodeEmporium
 29 How do neural networks work?
How do neural networks work?
CodeEmporium
 30 Evolution of Face Generation | Evolution of GANs
Evolution of Face Generation | Evolution of GANs
CodeEmporium
 31 How does Google Translate's AI work?
How does Google Translate's AI work?
CodeEmporium
 32 How to keep up with AI research?
How to keep up with AI research?
CodeEmporium
 33 How does YouTube recommend videos? - AI EXPLAINED!
How does YouTube recommend videos? - AI EXPLAINED!
CodeEmporium
 34 Variational Autoencoders - EXPLAINED!
Variational Autoencoders - EXPLAINED!
CodeEmporium
 35 Logistic Regression - VISUALIZED!
Logistic Regression - VISUALIZED!
CodeEmporium
 36 Gradient Descent - THE MATH YOU SHOULD KNOW
Gradient Descent - THE MATH YOU SHOULD KNOW
CodeEmporium
 37 Boosting - EXPLAINED!
Boosting - EXPLAINED!
CodeEmporium
 38 Transformer Neural Networks - EXPLAINED! (Attention is all you need)
Transformer Neural Networks - EXPLAINED! (Attention is all you need)
CodeEmporium
 39 Loss Functions - EXPLAINED!
Loss Functions - EXPLAINED!
CodeEmporium
 40 Optimizers - EXPLAINED!
Optimizers - EXPLAINED!
CodeEmporium
 41 NLP with Neural Networks & Transformers
NLP with Neural Networks & Transformers
CodeEmporium
 42 Batch Normalization - EXPLAINED!
Batch Normalization - EXPLAINED!
CodeEmporium
 43 Activation Functions - EXPLAINED!
Activation Functions - EXPLAINED!
CodeEmporium
 44 Data Scientist Answers Interview Questions
Data Scientist Answers Interview Questions
CodeEmporium
 45 Why use GPU with Neural Networks?
Why use GPU with Neural Networks?
CodeEmporium
 46 How do GPUs speed up Neural Network training?
How do GPUs speed up Neural Network training?
CodeEmporium
 47 BERT Neural Network - EXPLAINED!
BERT Neural Network - EXPLAINED!
CodeEmporium
 48 ConvNets Scaled Efficiently
ConvNets Scaled Efficiently
CodeEmporium
 49 Transformer Neural Net makes music! (JukeboxAI)
Transformer Neural Net makes music! (JukeboxAI)
CodeEmporium
 50 What do filters of Convolution Neural Network learn?
What do filters of Convolution Neural Network learn?
CodeEmporium
 51 We're hosting a Machine Learning Conference!
We're hosting a Machine Learning Conference!
CodeEmporium
 52 MLconfEU 2020: Machine Learning Conference for Software Engineers
MLconfEU 2020: Machine Learning Conference for Software Engineers
CodeEmporium
 53 Are Neural Networks Intelligent?
Are Neural Networks Intelligent?
CodeEmporium
 54 Time Series Forecasting with Machine Learning
Time Series Forecasting with Machine Learning
CodeEmporium
 55 Few Shot Learning - EXPLAINED!
Few Shot Learning - EXPLAINED!
CodeEmporium
 56 How does a Data Scientist Fight FRAUD?
How does a Data Scientist Fight FRAUD?
CodeEmporium
 57 How would a Data Scientist analyze Customer Churn?
How would a Data Scientist analyze Customer Churn?
CodeEmporium
 58 Expectations with Machine Learning
Expectations with Machine Learning
CodeEmporium
 59 Why Logistic Regression DOESN'T return probabilities?!
Why Logistic Regression DOESN'T return probabilities?!
CodeEmporium
 60 How you SHOULD code Machine Learning
How you SHOULD code Machine Learning
CodeEmporium

This video teaches the fundamentals of the Transformer model and how it translates text from one language to another, focusing on the encoder and decoder components. It explains how the model generates embeddings for words and uses them to sequentially generate translated words. Understanding this process is crucial for building and improving machine translation systems.

Key Takeaways
  1. Identify the key components of the Transformer model
  2. Explain how the encoder generates embeddings for words
  3. Describe how the decoder uses embeddings and previously generated words to generate the next word in the translation
  4. Understand how the sequence generation process works until the end of the sentence is reached
💡 The Transformer model uses a combination of encoder-generated embeddings and previously generated words to sequentially generate translated words, allowing for efficient and accurate machine translation.

Related AI Lessons

⚡
10 ChatGPT Prompts for Job Seekers: Resumes, Interviews & Career Growth
Learn how to leverage ChatGPT for job searching, resume building, and career growth with 10 actionable prompts
Medium · ChatGPT
 ⚡
Lost in Transcription: The Week the Machine Started Lying
Learn how Whisper AI transcription can be flawed and understand the importance of validation in AI-generated text
Medium · AI
 ⚡
How We Translate 300-Page Books Using Claude Without Hitting Token Limits
Learn how to translate long documents using Claude without hitting token limits by breaking them into overlapping chunks
Dev.to · 龚旭东
 ⚡
Building HITL Feedback RAG: Embeddings, Retrieval, and Reranking
Learn to build a Human-in-the-Loop (HITL) Feedback RAG system using embeddings, retrieval, and reranking to improve model performance
Medium · AI
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →