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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Ko'proq ko'rsatish📈 Telegram kanali Machine learning books and papers analitikasi
Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 506 obunachidan iborat bo'lib, Taʼlim toifasida 8 028-o'rinni va Eron mintaqasida 13 775-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
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📝 Tavsif va kontent siyosati
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“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
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24 506
Obunachilar
+524 soatlar
-147 kunlar
-10930 kunlar
Postlar arxiv
@Machine_learn
Gradient Centralization: A New Optimization Technique for Deep Neural Networks
Code: https://github.com/Yonghongwei/Gradient-Centralization
Paper: https://arxiv.org/abs/2004.01461
@Machine_learn
Flows for simultaneous manifold learning and density estimation
A new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that manifold.
Code: https://github.com/johannbrehmer/manifold-flow
Paper: https://arxiv.org/abs/2003.13913
@Machine_learn
Train transformer language models with reinforcement learning.
https://lvwerra.github.io/trl/
Code: https://github.com/openai/lm-human-preferences
Paper: https://arxiv.org/pdf/1909.08593.pdf
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1️⃣ @Ai_Tv
2️⃣ @AI_PYTHON
3️⃣ @HomeAi
علم داده:
1️⃣ @DataAnalysis
تحلیل داده و تصمیمگیری دادهمحور:
1️⃣ @Mr_IE
2️⃣ @python4finance
یادگیری ماشین:
1️⃣ @Machine_learn
آموزش پایتون و برنامه نویسی :
1️⃣ @pythony
2️⃣ @pythonchallenge
3️⃣ @raspberry_python
4️⃣ @Programming4all_0to100
@Machine_learn
Graph Isomorphism Software
Open-source software for finding isomorphism or canonical forms of graphs.
* Nauty/Traces
* Bliss
* saucy
* conauto
* Gi-ext
New paper by Yandex.MILAB 🎉
Tired of waiting for backprop to project your face into StyleGAN latent space to use some funny vector on it? Just distilate this tranformation by pix2pixHD!
arxiv.org/abs/2003.03581
@Machine_learn
Gradient boost trees with xgboost and scikit-learn #book #python
@Machine_learn
@Machine_learn
An AI program that plays Flappy Bird using reinforcement learning.
Code: https://github.com/taivu1998/FlapAI-Bird
Model: https://stanford-cs221.github.io/autumn2019-extra/posters/18.pdf
Paper: https://arxiv.org/abs/2003.09579
@Machine_learn
Rethinking Image Mixture for Unsupervised Visual Representation Learning
Code: https://github.com/szq0214/Rethinking-Image-Mixture-for-Unsupervised-Learning
Paper: https://arxiv.org/abs/2003.05438v1
@Machine_learn
Graph Machine Learning research groups: Le Song
Le Song (~1981)
- Affiliation: Georgia Institute of Technology;
- Education: Ph.D. at U. of Sydney in 2008 (supervised by Alex Smola);
- h-index: 59;
- Awards: best papers at ICML, NeurIPS, AISTATS;
- Interests: generative and adversarial graph models, social network analysis, diffusion models.
@Machine_learn
Anomaly detection with Keras, TensorFlow, and Deep Learning
In this tutorial, you will learn how to perform anomaly and outlier detection using autoencoders, Keras, and TensorFlow.
https://www.pyimagesearch.com/2020/03/02/anomaly-detection-with-keras-tensorflow-and-deep-learning/
A new paper from Samsung AI Center (Moscow) on unpaired image-to-image translation. Now – without any domain labels, even on training time!
▶️ youtu.be/DALQYKt-GJc
📝 arxiv.org/abs/2003.08791
📉 @Machine_learn
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1⃣ @Ai_Tv
2⃣ @AI_PYTHON
3⃣ @HomeAi
4⃣ @ailib
علم داده:
1⃣ @DataAnalysis
2⃣ @BigData_channel
تحلیل داده و تصمیمگیری دادهمحور:
1⃣ @Mr_IE
2⃣ @python4finance
یادگیری ماشین:
1⃣ @Machine_learn
آموزش پایتون و برنامه نویسی :
1⃣ @pythony
2⃣ @pythonchallenge
3⃣ @raspberry_python
4⃣ @Programming4all_0to100
@Machine_learn
Meta-Transfer Learning for Zero-Shot Super-Resolution
Code: https://github.com/JWSoh/MZSR
Paper: https://arxiv.org/abs/2002.12213v1
@Machine_learn
Fast and Easy Infinitely Wide Networks with Neural Tangents
https://ai.googleblog.com/2020/03/fast-and-easy-infinitely-wide-networks.html
Colab notebook: https://colab.research.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb#scrollTo=Lt74vgCVNN2b
Code: https://github.com/google/neural-tangents
Paper: https://arxiv.org/abs/1912.02803
@Machine_learn
Grid Search Optimization Algorithm in Python
https://stackabuse.com/grid-search-optimization-algorithm-in-python/
