По просьбе абитуриентов и студентов первых курсов палю свою годноту по матанализу. Простыми словами о сложном и подробно. Трехтомник Кудрявцева 🥰 #матан #кудрявцев
https://youtu.be/2Bw5f4vYL98 Physical simulations all done by AI? 5 minutes paper will explain how it works and show the results. What an incredible tool😍😍😍 no more sleepless nights with tons of differential equations to perform a single simulation.
YouTube
How Well Can DeepMind's AI Learn Physics? ⚛
❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers
📝 The paper "Learning to Simulate Complex Physics with Graph Networks" is available here:
https://arxiv.org/abs/2002.09405
https://sites.google.com/view/learning-to…
📝 The paper "Learning to Simulate Complex Physics with Graph Networks" is available here:
https://arxiv.org/abs/2002.09405
https://sites.google.com/view/learning-to…
Просто нет слов насколько качественно и доступно в этом блоге написаны посты)
https://lilianweng.github.io/lil-log/
https://lilianweng.github.io/lil-log/
Forwarded from VolodymyrGavrysh
Теорія ймовірностей 2.2 - Підготовчі курси на програму «Науки про дані» 2020. - YouTube
https://www.youtube.com/watch?v=B9rkQ-wMXQg&list=PLr1w0qwTp9lB5NohQd3sA1SVxEEp8mjYv
https://www.youtube.com/watch?v=B9rkQ-wMXQg&list=PLr1w0qwTp9lB5NohQd3sA1SVxEEp8mjYv
YouTube
Теорія ймовірностей 2.2 - Підготовчі курси на програму «Науки про дані» 2020.
Am awesome tutorial on Time Series machine learning and digital signal processing:
Feature engineering with Autocorrelation, Fast Fourier Transform , Power Spectral Density functions and Random Forest Classifier as the model. https://ataspinar.com/2018/04/04/machine-learning-with-signal-processing-techniques/
Feature engineering with Autocorrelation, Fast Fourier Transform , Power Spectral Density functions and Random Forest Classifier as the model. https://ataspinar.com/2018/04/04/machine-learning-with-signal-processing-techniques/
ML Fundamentals
Machine Learning with Signal Processing Techniques
[latexpage] Introduction Stochastic Signal Analysis is a field of science concerned with the processing, modification and analysis of (stochastic) signals. Anyone with a background in Physics or En…
A guide for using the Wavelet Transform in Machine Learning
https://ataspinar.com/2018/12/21/a-guide-for-using-the-wavelet-transform-in-machine-learning/
https://ataspinar.com/2018/12/21/a-guide-for-using-the-wavelet-transform-in-machine-learning/
ML Fundamentals
A guide for using the Wavelet Transform in Machine Learning
[latexpage] 1. Introduction In a previous blog-post we have seen how we can use Signal Processing techniques for the classification of time-series and signals. A very short summary of that post is:…
Happy New Year, folks!
while not Singularity:https://www.youtube.com/watch?v=HL7DEkXV_60
year += 1
YouTube
2020's Biggest Breakthroughs in Math and Computer Science
For mathematicians and computer scientists, 2020 was full of discipline-spanning discoveries and celebrations of creativity. We'd like to take a moment to re...
https://arxiv.org/abs/2006.00712 - My favorite SOTA Neural Network now is able to compute the Holographic Quantum Chromodynamic)
The neural ordinary differential equation (Neural ODE) is a novel machine learning architecture whose weights are smooth functions of the continuous depth. We apply the Neural ODE to holographic QCD by regarding the weight functions as a bulk metric, and train the machine with lattice QCD data of chiral condensate at finite temperature. The machine finds consistent bulk geometry at various values of temperature and discovers the emergent black hole horizon in the holographic bulk automatically. The holographic Wilson loops calculated with the emergent machine-learned bulk spacetime have consistent temperature dependence of confinement and Debye-screening behavior. In machine learning models with physically interpretable weights, the Neural ODE frees us from discretization artifact leading to difficult ingenuity of hyperparameters, and improves numerical accuracy to make the model more trustworthy.
https://arxiv.org/abs/2011.05364 - Gaussian ODE😍😍😍❤️ Learning ODE Models with Qualitative Structure Using Gaussian Processes
Recent advances in learning techniques have enabled the modelling of dynamical systems for scientific and engineering applications directly from data. However, in many contexts, explicit data collection is expensive and learning algorithms must be data-efficient to be feasible. This suggests using additional qualitative information about the system, which is often available from prior experiments or domain knowledge. In this paper, we propose an approach to learning the vector field of differential equations using sparse Gaussian Processes that allows us to combine data and additional structural information, like Lie Group symmetries and fixed points, as well as known input transformations. We show that this combination improves extrapolation performance and long-term behaviour significantly, while also reducing the computational cost.
Applying GANs for the Time Sereies forecasting problem? Why not: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8717640 https://git.opendfki.de/koochali/forgan
GitLab
Alireza Koochali / ForGAN · GitLab
GitLab Community Edition
Forwarded from ✙ Fatum ✙
ICLR 2019 | ‘Fast as Adam & Good as SGD’ — New Optimizer Has Both | by Synced | SyncedReview | Medium
https://medium.com/syncedreview/iclr-2019-fast-as-adam-good-as-sgd-new-optimizer-has-both-78e37e8f9a34
https://medium.com/syncedreview/iclr-2019-fast-as-adam-good-as-sgd-new-optimizer-has-both-78e37e8f9a34
Medium
ICLR 2019 | ‘Fast as Adam & Good as SGD’ — New Optimizer Has Both
The optimization algorithm (or optimizer) is the main approach used today for training a machine learning model to minimize its error rate…
https://github.com/Luolc/AdaBound - a nice and neat pytorch extension for AdaBound Oprimizer
GitHub
GitHub - Luolc/AdaBound: An optimizer that trains as fast as Adam and as good as SGD.
An optimizer that trains as fast as Adam and as good as SGD. - Luolc/AdaBound
milets19_poster_4.pdf
804.9 KB
https://milets19.github.io/papers/milets19_poster_4.pdf DenseNet for Time Series Classification. Very useful paper, describes both various preprocessing types and architectures of the Dense blocks,then compares performances and scores. Strongly recommend for practitioners #timeseries #DenseNet
Who needs fancy DenseNets, EfficientNets, NasNets and so on if you have THIS: Making ResNets Great Again!
https://arxiv.org/pdf/2103.07579.pdf
https://arxiv.org/pdf/2103.07579.pdf
https://youtu.be/ABbDB6xri8o Tesla AI pipeline, sooo huge and so amazing. Take a look.
P.S. something similar I’m trying to build in my start up, but yet I’m not even close
P.S. something similar I’m trying to build in my start up, but yet I’m not even close
YouTube
Tesla AI Day Highlights | Lex Fridman
A quick video on the main innovations presented at Tesla AI day.
What the full livestream here: https://www.youtube.com/watch?v=j0z4FweCy4M
OUTLINE:
0:00 - Overview
1:16 - Neural network architecture
4:55 - Data and annotation
6:44 - Autopilot & DOJO
8:28…
What the full livestream here: https://www.youtube.com/watch?v=j0z4FweCy4M
OUTLINE:
0:00 - Overview
1:16 - Neural network architecture
4:55 - Data and annotation
6:44 - Autopilot & DOJO
8:28…
Forwarded from Мишин Лернинг 🇺🇦🇮🇱
Нейросеть Codex от OpenAI: увольняйте ваших Data Scientist’ов
Будущее наступило! Нейросеть Codex (gpt 3 для генерации кода) позволяет решать data science задачи на естественном языке!
https://youtu.be/Ru5fQZ714x8
Будущее наступило! Нейросеть Codex (gpt 3 для генерации кода) позволяет решать data science задачи на естественном языке!
https://youtu.be/Ru5fQZ714x8
YouTube
Data Science with OpenAI Codex
Learn more: https://openai.com/blog/openai-codex