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Machine learning books and papers
Kanalga Telegram’da o‘tish
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 24 505 obunachidan iborat bo'lib, Taʼlim toifasida 8 033-o'rinni va Eron mintaqasida 13 749-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 505 obunachiga ega bo‘ldi.
03 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -99 ga, so‘nggi 24 soatda esa 2 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.54% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.24% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 603 marta ko‘riladi; birinchi sutkada odatda 549 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 1 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 04 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.
24 505
Obunachilar
+224 soatlar
-107 kunlar
-9930 kunlar
Postlar arxiv
discriminative :
1:#Regression
2:#Logistic regression
3:#decision tree(Hunt)
4:#neural network(traditional network, deep network)
5:#Support Vector Machine(SVM)
Generative:
1:#Hidden Markov model
2:#Naive bayes
3:#K-nearest neighbor(KNN)
4:#Generative adversarial networks(GANs)
Deep learning:
1:CNN
R_CNN
Fast-RCNN
Mask-RCNN
2:RNN
3:LSTM
4:CapsuleNet
5:Siamese:
siamese cnn
siamese lstm
siamese bi-lstm
siamese CapsuleNet
6:time series data
SVR
DT(cart)
Random Forest linear
Bagging
Boosting
جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید
@Raminmousa
@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
@Machine_learn
The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.
code: https://github.com/salesforce/ctrl
article: https://einstein.ai/presentations/ctrl.pdf
C-write:ai_machinelearning_big_data
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/
@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
How normalization applied to layers helps to reach faster convergence.
ArXiV: https://arxiv.org/abs/1502.03167
#NeuralNetwork #nn #normalization #DL
Deep Learning with Python
The ultimate beginners guide to Learn Deep Learning with Python Step by Step
#book #DL #python
@Machine_learn
Deep Learning with Python
The ultimate beginners guide to Learn Deep Learning with Python Step by Step
#book #DL #python
@Machine_learn
@Machine_learn
DeepMind's OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
code: https://github.com/deepmind/open_spiel
article: https://arxiv.org/abs/1908.09453
@Machine_learn
🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae
Code: https://github.com/tensorflow/gan
Free course: https://developers.google.com/machine-learning/gan/
Paper: https://arxiv.org/abs/1805.08318
@Machine_learn
Memory-Efficient Adaptive Optimization
Source: https://arxiv.org/abs/1901.11150
Code: https://github.com/google-research/google-research/tree/master/sm3
@Machine_leaen
ai ,machine learning
#code #datasets #paper
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code
https://paperswithcode.com/sota
@Machine_learn
Rank-consistent Ordinal Regression for Neural Networks
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
@Machine_learn
The HSIC Bottleneck: Deep Learning without Back-Propagation🥺
An alternative to conventional backpropagation, that has a number of distinct advantages.
Link: https://arxiv.org/abs/1908.01580
#backpropagation #DL
@Machine_learn
How to Implement Progressive Growing GAN Models in Keras
https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/
@Machine_learn
Interpreting Latent Space of GANs for Semantic Face Editing
https://shenyujun.github.io/InterFaceGAN/
code: https://github.com/ShenYujun/InterFaceGAN.git
