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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 24 502 obunachidan iborat bo'lib, Taʼlim toifasida 8 028-o'rinni va Eron mintaqasida 13 775-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 502 obunachiga ega bo‘ldi.
02 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -109 ga, so‘nggi 24 soatda esa 5 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Post qamrovi: Har bir post o‘rtacha 1 541 marta ko‘riladi; birinchi sutkada odatda 500 ta ko‘rish yig‘iladi.
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- Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
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“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
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24 502
Obunachilar
+524 soatlar
-147 kunlar
-10930 kunlar
Postlar arxiv
Machine Learning for Computer Architecture
http://ai.googleblog.com/2021/02/machine-learning-for-computer.html
@Machine_learn
WeNet open source, production first and production ready end-to-end (E2E) speech recognition toolkit
Github: https://github.com/mobvoi/wenet
Paper: https://arxiv.org/abs/2102.01547v1
Tutorial: https://github.com/mobvoi/wenet/blob/main/docs/tutorial.md
@Machine_learn
Open Datasets for Research
During last week there were several news about newly open datasets for researchers.
1. Twitter opened “full history of public conversation” for academics (specifically, for academics):
https://www.theverge.com/2021/1/26/22250203/twitter-academic-research-public-tweet-archive-free-access
We can happily conduct researches about social networks graphs, users behavior and fake news (especially fake news🙃) without fighting with Twitter API.
2. Papers with code are now also Papers with Datasets:
https://www.paperswithcode.com/datasets
Not for only NLP, but for all fields structured for easy search and download.
@Machine_learn
Feature Engineering for Machine Learning
Principles and Techniques for Data Scientists
#book
@Machine_learn
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE #paper @Machine_learn
Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
http://ai.googleblog.com/2021/02/evaluating-design-trade-offs-in-visual.html
@Machin_learn
Improving Mobile App Accessibility with Icon Detection
http://ai.googleblog.com/2021/01/improving-mobile-app-accessibility-with.html
@Machine_learn
TIN: Transferable Interactiveness Network
Github: https://github.com/DirtyHarryLYL/Transferable-Interactiveness-Network
HOI-Learning-List: https://github.com/DirtyHarryLYL/HOI-Learning-List
Paper: https://arxiv.org/abs/2101.10292v1
@Machine_learn
🔸لیستی از برترین کانالهای آموزشی در زمینه های هوشمصنوعی, پایتون و یادگیری ماشین
❯ هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAI
❯ یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
2⃣ @cvision
❯ علم داده:
1⃣ @mr_ie
❯ آموزش پایتون و برنامه نویسی :
1⃣ @pythony
2⃣ @pythonchallenge
3⃣ @Programming4all_0to100
Stabilizing Live Speech Translation in Google Translate
http://ai.googleblog.com/2021/01/stabilizing-live-speech-translation-in.html
@Machine_learn
Self-Adaptive Training
Github: https://github.com/LayneH/self-adaptive-training
Paper: https://arxiv.org/abs/2101.08732v1
@Machine_learn
سلام از دوستان كسي هست كه به #رايانش_تكاملي مسلط باشه ممنون ميشم بهم پيام بده
@Raminmousa
A Visual Intro to NumPy and Data Representation
.
Link : https://jalammar.github.io/visual-numpy/
@Machine_learn
👉Lecture Notes for Linear Algebra Featuring Python
.
GitHub link : https://github.com/MacroAnalyst/Linear_Algebra_With_Python
@Machine_learn
🔥 Fast convolutional neural networks on FPGAs with hls4ml
Github: https://github.com/fastmachinelearning/hls4ml
Paper: https://arxiv.org/abs/2101.05108v1
Documentation: https://fastmachinelearning.org/hls4ml/
@Machine_learn
Alex_Thomas_Natural_Language_Processing_with_Spark_NLP_Learn.pdf
#book #NLP
@Machine_learn
Superpixel-based Refinement for Object Proposal Generation
Github: https://github.com/chwilms/superpixelRefinement
Paper: https://arxiv.org/abs/2101.04574v1
@Machine_learn
Gender recognition in the wild: a robustness evaluation over corrupted images
Github: https://github.com/MiviaLab/GenderRecognitionFramework
Paper: https://link.springer.com/article/10.1007/s12652-020-02750-0
@Machine_lear
