tsBNgen, a Python Library to Generate Synthetic Data From an Arbitrary Bayesian Network.
Paper Summary: https://www.marktechpost.com/2020/09/15/tsbngen-a-python-library-to-generate-synthetic-data-from-an-arbitrary-bayesian-network/
Paper: https://arxiv.org/pdf/2009.04595.pdf
Github: https://github.com/manitadayon/tsBNgen/blob/master/tsbngen.pdf
Paper Summary: https://www.marktechpost.com/2020/09/15/tsbngen-a-python-library-to-generate-synthetic-data-from-an-arbitrary-bayesian-network/
Paper: https://arxiv.org/pdf/2009.04595.pdf
Github: https://github.com/manitadayon/tsBNgen/blob/master/tsbngen.pdf
MarkTechPost
tsBNgen, a Python Library to Generate Synthetic Data From an Arbitrary Bayesian Network.
tsBNgen, a Python Library to Generate Time Series and Sequential Data Based on an Arbitrary Dynamic Bayesian Network.
MiniTorch
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python
Sasha Rush and Ge Gao : https://minitorch.github.io/index.html
#DeepLearning #PyTorch #Python
Very nice article by M. Aminian in SIAM News ennoscriptd "The Connection Between Applied Mathematics and Deep Learning".
The article dissects and explains in very clear terms the gist of my plenary talk at the recent SIAM Mathematics of Data Science conference ennoscriptd "The Deep Learning - Applied Mathematics Connection".
It discusses the specifics of optimization as it pertains to DL (stochastic optimization in high-dimensional spaces), the surprising fact that widely overparamterized DL models work well, the use of DL in traditional applications of scientific computing: PDE solving, lattice quantum computation, graph convolutional nets and the connection with Fourier transform, and the rising topic of self-supervised learning.
The methods of Applied Mathematics could help explain some of the mysteries of deep learning
SIAM News paper: https://sinews.siam.org/Details-Page/the-connection-between-applied-mathematics-and-deep-learning
Video of my talk: https://youtu.be/9sqoe_krQ1E
The article dissects and explains in very clear terms the gist of my plenary talk at the recent SIAM Mathematics of Data Science conference ennoscriptd "The Deep Learning - Applied Mathematics Connection".
It discusses the specifics of optimization as it pertains to DL (stochastic optimization in high-dimensional spaces), the surprising fact that widely overparamterized DL models work well, the use of DL in traditional applications of scientific computing: PDE solving, lattice quantum computation, graph convolutional nets and the connection with Fourier transform, and the rising topic of self-supervised learning.
The methods of Applied Mathematics could help explain some of the mysteries of deep learning
SIAM News paper: https://sinews.siam.org/Details-Page/the-connection-between-applied-mathematics-and-deep-learning
Video of my talk: https://youtu.be/9sqoe_krQ1E
SIAM News
The Connection Between Applied Mathematics and Deep Learning
By Manuchehr Aminian
In recent years, deep learning (DL) has inspired a myriad of advances within the scientific computing community. This subset of artificial intelligence relies on multiple components of applied mathematics, but what type of relationship…
In recent years, deep learning (DL) has inspired a myriad of advances within the scientific computing community. This subset of artificial intelligence relies on multiple components of applied mathematics, but what type of relationship…
Papers with Code partners with arXiv
Robert Stojnic : https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv-ecc362883167
@ArtificialIntelligenceArticles
#MachineLearning #arXiv #Code
Robert Stojnic : https://medium.com/paperswithcode/papers-with-code-partners-with-arxiv-ecc362883167
@ArtificialIntelligenceArticles
#MachineLearning #arXiv #Code
Deep Reinforcement Learning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
CS 285 at UC Berkeley, Fall 2020
Levine et al.: https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
YouTube
Deep Reinforcement Learning: CS 285 Fall 2020
Lectures for UC Berkeley CS 285: Deep Reinforcement Learning.
State of AI Report 2020
The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future.
The Report is produced by AI investors Nathan Benaich and Ian Hogarth.
https://www.stateof.ai/
The State of AI Report analyses the most interesting developments in AI. We aim to trigger an informed conversation about the state of AI and its implication for the future.
The Report is produced by AI investors Nathan Benaich and Ian Hogarth.
https://www.stateof.ai/
www.stateof.ai
State of AI Report 2025
The State of AI Report analyses the most interesting developments in AI. Read and download here.
https://www.confetti.ai/
Confetti AI | Ace Your Machine Learning Interviews
Confetti AI | Ace Your Machine Learning Interviews