"So, without further ado, let’s dive into how attention-weighting and FFN make transformers so powerful." Srijanie Dey, PhD's latest post is an accessible introduction to the exciting world of transformers and the math behind them.
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If you would like to understand Math behind a crucial block of Transformers, I explained it in my First Medium article.
Decoding “Attention is all you need”
medium.com
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From its underlying math to a hands-on, from-scratch implementation, Cristian Leo's new deep dive is a comprehensive introduction to the inner workings of transformers' multi-head attention.
The Math Behind Multi-Head Attention in Transformers
towardsdatascience.com
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Data Scientist and Machine Learning Engineer| Machine Learning, Deep Learning and NLP | Cloud Certified (Azure, AWS)| Passionate about solving practical business problems with data and AI.
Lovely introduction to transformes base on recent history of DL. I've actually learned Deep Learning as the "prehistoric age" way: https://lnkd.in/d-gtps_2
Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy
https://www.youtube.com/
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For a powerful blend of math and code, don't miss Cristian Leo's comprehensive introduction to KANs (Kolmogorov-Arnold networks), which explains in detail how they've come to surpass multi-layer perceptrons in accuracy and interpretability.
The Math Behind KAN — Kolmogorov-Arnold Networks
towardsdatascience.com
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Full Stack Software Consultant ✔Web Apps ✔Java ✔WebGIS ✔Flutter (Mobile, Web) ✔Aeronautical Information Systems
https://lnkd.in/gzX5B5bE The flesh is composed of self-contained non-technical mainstream explanations and examples of the field of mathematics which deals with meaning, called Model Theory. #ai #artificialintelligence #machinelearning #deeplearning #philosophyofmathematics #modeltheory #mathematicallogic
Hack, Hack, Who's There? A Gentle Introduction to Model Theory
freecomputerbooks.com
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I have just released a video on Recurrent Neural Networks (RNNs). RNNs are at the heart of numerous breakthroughs in AI, particularly in processing and predicting sequential data. The video covers the concept and the math behind RNN. Here is the video link: https://lnkd.in/gFZVtDUm #rnn #ai #deeplearning
RNN Theory and Math Clearly Explained
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In the latest installment of his "Math behind..." series, Cristian Leo leads us on a detailed exploration of batch normalization, its underlying mathematics, and its from-scratch implementation.
The Math Behind Batch Normalization
towardsdatascience.com
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'Attention Is All You Need' by Ashish Vaswani et al. in 2017 made a revolutionary impact on Natural Language Processing (NLP) by introducing the Transformer architecture. Nowadays, this architecture is widely used across various domains, with its core structure remaining untouched. It's surprising that, at its introduction, the authors were unaware of it's impact, given the continuous growth of deep learning approaches since then. This video from Andrej Karpathy gives an excellent introduction to Transformers and also it's historical context. Do watch it if you are interested :) CS25 I Stanford Seminar - Transformers United 2023: Introduction to Transformers w/ Andrej Karpathy
Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy
https://www.youtube.com/
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One of the best videos I've seen explaining how transformers work for audiences with an undergraduate mathematics education or anyone who has had some exposure to linear algebra and matrix multiplication. Highly recommend checking this out if you want to go deeper on transformers without diving into white papers like "Attention is all you need" https://lnkd.in/e5iSwKk9
But what is a GPT? Visual intro to transformers | Chapter 5, Deep Learning
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🚀 Exciting news in the world of Computer Vision! A new method has been proposed to reduce halo artifacts in Local Histogram Equalization algorithms, resulting in visually natural images. This approach leverages insights from the human visual system to address dark and light variants separately. #ComputerVision #ImageProcessing #AI #ML #TechInnovation
🚀 Exciting news in the world of Computer Vision! A new method has been proposed to reduce halo artifacts in Local Histogram Equalization algorithms, resulting in visually natural images. This approach leverages insights from the human visual system to address dark and light variants separately. #ComputerVision #ImageProcessing #AI #ML #TechInnovation
arxiv.org
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