Blowing up the Transformer Encoder!

CodeEmporium · Advanced ·📐 ML Fundamentals ·3y ago

Key Takeaways

The video explores the transformer encoder architecture, diving deep into its components and functionality, with references to the original transformer paper and related resources.

Original Description

Let's deep dive into the transformer encoder architecture. ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [ 1🔎] My playlist for all transformer videos before this: https://www.youtube.com/watch?v=QCJQG4DuHT0&list=PLTl9hO2Oobd97qfWC40gOSU8C0iu0m2l4 [ 2 🔎] Transformer Main Paper: https://arxiv.org/abs/1706.03762 PLAYLISTS FROM MY CHANNEL ⭕ ChatGPT Playlist of all other videos: https://youtube.com/playlist?list=PLTl9hO2Oobd9coYT6XsTraTBo4pL1j4HJ ⭕ Transformer Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ Convolutional Neural Networks: https://youtube.com/playlist?list=PLTl9hO2Oobd9U0XHz62Lw6EgIMkQpfz74 ⭕ The Math You Should Know : https://youtube.com/playlist?list=PLTl9hO2Oobd-_5sGLnbgE8Poer1Xjzz4h ⭕ Probability Theory for Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd9bPcq0fj91Jgk_-h1H_W3V ⭕ Coding Machine Learning: https://youtube.com/playlist?list=PLTl9hO2Oobd82vcsOnvCNzxrZOlrz3RiD MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: https://imp.i384100.net/Deep-Learning 📕 Python for Everybody: https://imp.i384100.net/python 📕 MLOps Course: https://imp.i384100.net/MLOps 📕 Natural Language Processing (NLP): https://imp.i384100.net/NLP 📕 Machine Learning in Production: https://imp.i384100.net/MLProduction 📕 Data Science Specialization: https://imp.i384100.net/DataScience 📕 Tensorflow: ht
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Playlist

Uploads from CodeEmporium · CodeEmporium · 0 of 60

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

Key Takeaways
  1. Understand the transformer encoder architecture
  2. Learn about self-attention mechanisms
  3. Study the transformer paper and its applications
  4. Explore related resources and courses
  5. Implement transformer models using popular libraries
💡 The transformer encoder architecture is a crucial component of modern natural language processing models, and understanding its functionality and applications is essential for building effective ML models.

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