📖 "A Little Book on the Fundamentals of Generative AI" - an intuitive introduction to the mathematics:
arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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arxiv.org/pdf/2605.29713
#GenerativeAI #Mathematics #DeepLearning #AIResearch #MachineLearning #arXiv
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A collection of explanations for key concepts and scientific papers in machine learning:
https://github.com/dair-ai/ML-Papers-Explained
https://github.com/dair-ai/ML-Papers-Explained
GitHub
GitHub - dair-ai/ML-Papers-Explained: Explanation to key concepts in ML
Explanation to key concepts in ML. Contribute to dair-ai/ML-Papers-Explained development by creating an account on GitHub.
👍7❤2
"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://t.me/CodeProgrammer🤩
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
https://t.me/CodeProgrammer
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❤10
Forwarded from Machine Learning with Python
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❤4
Forwarded from Machine Learning
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I found a great resource for interactive learning about machine learning and AI – VizLearn.
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://t.me/MachineLearning9
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
https://t.me/MachineLearning9
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• Your users don't need an API key, and you don't pay AI bills: visitors pay per use
• You get a share of every paid AI call in your app
• Free hosting during the beta
• We help you move the app over
We're inviting the first creators of the beta. Best fit: a front-end app where AI is the main feature, even if it never left localhost.
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Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.
Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
https://t.me/CodeProgrammer
#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding
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Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
https://t.me/CodeProgrammer
#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding
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Forwarded from Machine Learning
This collection includes:
torch.nn;torch.compile
and ONNX;
A useful resource for both beginners and those already working with PyTorch in production.
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❤7👍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
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"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
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📚 Download 10 Python books for FREE:
1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65
#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding
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1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65
#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding
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❤7
"Graph Theory with Applications" is a free textbook that provides a comprehensive introduction to graph theory and its practical applications.
The book begins with basic concepts: graphs, subgraphs, paths, cycles, trees, and connectivity. It then moves on to more advanced topics: Eulerian and Hamiltonian graphs, matching, graph coloring, planar and directed graphs, network flows, and extremal graph theory.
What is particularly useful is that the authors combine a visual, geometric, and topological understanding of graphs with a rigorous presentation of the mathematical foundations.
This makes the book a valuable resource for studying the theory that underlies many modern applications of graphs in computer science, computer networks, optimization, data analysis, and related fields.
Book: https://book.huihoo.com/pdf/graph-theory-with-applications.pdf
https://t.me/CodeProgrammer
The book begins with basic concepts: graphs, subgraphs, paths, cycles, trees, and connectivity. It then moves on to more advanced topics: Eulerian and Hamiltonian graphs, matching, graph coloring, planar and directed graphs, network flows, and extremal graph theory.
What is particularly useful is that the authors combine a visual, geometric, and topological understanding of graphs with a rigorous presentation of the mathematical foundations.
This makes the book a valuable resource for studying the theory that underlies many modern applications of graphs in computer science, computer networks, optimization, data analysis, and related fields.
Book: https://book.huihoo.com/pdf/graph-theory-with-applications.pdf
https://t.me/CodeProgrammer
❤5
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Forwarded from Machine Learning
🔖 A Comprehensive Resource for Machine Learning Practice
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Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓ Link to the platform
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The platform offers:
🫡 1200+ machine learning tasks;
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🫡 Deep Learning, NLP, and Computer Vision;
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Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓ Link to the platform
https://www.deep-ml.com/
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