KAN Practical Implementation (Kolmogorov–Arnold Networks Algorithm)
Skills:
ML Maths Basics70%
#kan #Kolmogorov–ArnoldNetworks #mlp #deeplearning #machinelearning #ai
In this video, I tried to implement Kolmogorov–Arnold Networks (KAN) Algorithm using imodelsx library.
The KAN is an approach in the field of machine learning that is based on the Kolmogorov-Arnold representation theorem from mathematical analysis. This method applies the theorem's insights to build predictive models for complex, high-dimensional datasets. KAN uses the idea that any multivariate function can be decomposed into sums and compositions of univariate functions.
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You can access the notebook on KAN implementation of the video from here: https://github.com/manishasirsat/kan-implementation
You can watch a video on KAN: Kolmogorov–Arnold Networks Paper Explained from here: https://www.youtube.com/watch?v=AyM9YqU923k
Github repository for the code: https://github.com/manishasirsat/rag-llm-demo
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Generative AI Playlist: https://www.youtube.com/watch?v=ID04YmgzM38&list=PLzkBTicHqQFmdF62zHHramnBRZl6zUvmR
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Connect with me on social media platforms:
Website: https://ai-researchstudies.com/
Google scholar: https://scholar.google.com/citations?user=kM4QN-8AAAAJ&hl=en
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GitHub:https://github.com/manishasirsat
Quora: https://machinelearningresearch.quora.com/
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Twitter: https://twitter.com/ManishaSirsat
⏱️ Timestamps
0:00 Intro
0:33 Problem statement
1:40 'imodelsx' python library
2:07 Agenda
3:35 Data processing
5:10 KAN implemen
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Chapters (6)
Intro
0:33
Problem statement
1:40
'imodelsx' python library
2:07
Agenda
3:35
Data processing
5:10
KAN implemen
🎓
Tutor Explanation
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