"Dive into Deep Learning" 📘🤖 is an open-source book that forms the mathematical foundation for large language models. 🧠📐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. 🧮📉🔄
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. 🚀🔗🧠
It contains over 1,000 pages 📖 and provides clear explanations, practical examples, and exercises. ✅📝 Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. 🌐🔍🤖
arxiv.org/pdf/2106.11342 🔗
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤9👍5👎1😁1
Forwarded from Data Analytics
One of the key moments when I truly understood how transformers work: 🧠✨
"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."
"Start thinking of it as a residual flow." 🌊
Each block in a transformer has a residual connection that adds the input of the block to its output:
Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕
Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.
Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗
I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework
#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."
"Start thinking of it as a residual flow." 🌊
Each block in a transformer has a residual connection that adds the input of the block to its output:
x' = f(x) + x
Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕
Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions.
Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗
I owe these ideas to an older article by Anthropic about the architecture of transformers:
transformer-circuits.pub/2021/framework
#Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤7👍1