uz
Feedback
Machine learning books and papers

Machine learning books and papers

Kanalga Telegram’da o‘tish

📈 Telegram kanali Machine learning books and papers analitikasi

Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 508 obunachidan iborat bo'lib, Taʼlim toifasida 8 019-o'rinni va Eron mintaqasida 13 748-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 24 508 obunachiga ega bo‘ldi.

04 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -101 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 6.50% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.21% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 1 594 marta ko‘riladi; birinchi sutkada odatda 541 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn

Yuqori yangilanish chastotasi (oxirgi ma’lumot 05 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

24 508
Obunachilar
+324 soatlar
-97 kunlar
-10130 kunlar
Postlar arxiv
#Chest Radiograph Pathology Categorization via Transfer Learning #Chapter13 @Machine_learn

#Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning #Chapter11 @Machine_learn

#Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching #Chapter9 @Machine_learn

#Deep Learning Tissue Segmentation in Cardiac Histopathology Images #Chapter8 @Machine_learn

#Deep Voting and Structured Regression for Microscopy Image Analysis #Chapter7 @Machine_learn

#Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images #Chapter6 @Machine_learn

#Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks #Chapter5 @Machine_learn

#Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition #Chapter4 @Machine_learn

#An Introduction to Deep Convolutional Neural Nets for Computer Vision #Chapter2 @Machine_learn

#An Introduction to Neural Networks and Deep Learning #Chapter1 @Machine_learn

#deep learning adaptive computation #book @Machine_learn

#learning predictive analytics with python #book #Machine_learn

#Datascience #MachineLearning #Artificialintelligence #Statistics
#Datascience #MachineLearning #Artificialintelligence #Statistics

p.y.b: Here is a list of what I believe are the 10 Practical Steps for #DataScience: 1. Programming a. Python - https://lnkd.in/gGQ7cuv b. R - https://lnkd.in/giMGbph c. SQL - https://lnkd.in/gM8nMNP d. Command Line - https://lnkd.in/e3EQuis 2. Stats/Prob/Math a. Coursera's Statistics w/ R - https://lnkd.in/gGT9NEf b. edX's Probability - https://lnkd.in/gpUyC3P c. Khan Academy Linear Algebra - https://lnkd.in/gMshbX4 3. Data Viz a. Python Matplotlib- https://lnkd.in/gr3ifNt b. R ggplot2 - https://lnkd.in/eThJXNr 4. Data Manipulation a. Python Pandas - https://lnkd.in/g9kfpX4 b. R dplyr - https://lnkd.in/gAWusih 5. #MachineLearning a. Google Crash Course - https://lnkd.in/gSgkVcT b. Stanford Coursera - https://lnkd.in/g8ZG557 c. ISLR Book - https://lnkd.in/gk8GPZC 6. Experimental Design a. Udacity A/B Testing - https://lnkd.in/gCerh4f 7. Business Sense a. Metrics - https://lnkd.in/gZAG7bS 8. Communication a. Storytelling - https://lnkd.in/gwjxVUu 9. Profile Building a. GitHub - https://lnkd.in/g4r9naJ b. LinkedIn - https://lnkd.in/g-KHHEC c. Kaggle - https://lnkd.in/gBC77Hu d. DS Resume - https://lnkd.in/gU8WVAF 🏅 10. Job Search a. Daily Expert Tips & Advice - https://lnkd.in/g8z-xXD --- Hope this helps! 👍 Updated on my site - http://www.claoudml.co/

#Adrian_Rosebrock #deep_Learning #book @Machine_learn

#Reinforcement Learning Textbook - Sutton #book @Machine_learn

سلام از دوستان اگر کسی پایان نامش مرتبط با موضوع«بهبود استخراج قوانین انجمني با استفاده از روش های تکاملي» هستش لطفا جهت همکاری به این ایدی پیام بدن. با تشکر @mahdi7_7_7

#LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS - ICLR 2019 @Machine_learn

#deep learning and convolutional #book @Machine_learn

#deep learning adaptive comoutation #book @Machine_learn