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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Show more📈 Analytical overview of Telegram channel Machine learning books and papers
Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 506 subscribers, ranking 8 028 in the Education category and 13 775 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 506 subscribers.
According to the latest data from 02 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -109 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.29%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 541 views. Within the first day, a publication typically gains 500 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as disorder, psy, مقاله, framework, graph.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 03 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
24 506
Subscribers
+524 hours
-147 days
-10930 days
Posts Archive
Machine Learning and Security — C. Chio, D. Freeman (en) 2018
#book #ML
@Machine_learn
AI & Art
@Machine_learn
some artist use the large collections of #data & #ML #algorithms to create mesmerizing & dynamic #installations
watch the video —> https://youtu.be/I-EIVlHvHRM
Paper link:
https://arxiv.org/pdf/1904.02689.pdf
Github repo:
https://github.com/dbolya/yolact
YOLACT (You Only Look At CoefficienTs) - Real-time Instance Segmentation
Results are impressive, above 30 FPS on COCO test-dev
Practical Computer Vision Applications Using Deep Learning with CNNs — Ahmed Fawzy Gad (en) 2018
@Machine_learn
Uber AI Plug and Play Language Model (PPLM)
PPLM allows a user to flexibly plug in one or more simple attribute models representing the desired control objective into a large, unconditional language modeling (LM). The method has the key property that it uses the LM as is – no training or fine-tuning is required – which enables researchers to leverage best-in-class LMs even if they don't have the extensive hardware required to train them.
PPLM lets users combine small attribute models with an LM to steer its generation. Attribute models can be 100k times smaller than the LM and still be effective in steering it
PPLM algorithm entails three simple steps to generate a sample:
* given a partially generated sentence, compute log(p(x)) and log(p(a|x)) and the gradients of each with respect to the hidden representation of the underlying language model. These quantities are both available using an efficient forward and backward pass of both models;
* use the gradients to move the hidden representation of the language model a small step in the direction of increasing log(p(a|x)) and increasing log(p(x));
* sample the next word
more at paper: https://arxiv.org/abs/1912.02164
blogpost: https://eng.uber.com/pplm/
code: https://github.com/uber-research/PPLM
online demo: https://transformer.huggingface.co/model/pplm
@Machine_learn
#nlp #lm #languagemodeling #uber #pplm
Learning Singing From Speech
Article: https://arxiv.org/abs/1912.10128
Example: https://tencent-ailab.github.io/learning_singing_from_speech/
# Histogram-based Outlier Score (HBOS): A fastUnsupervised Anomaly Detection Algorithm #code #HBOS #Anomaly_Detection
رویکرد HBOS یک رویکرد بدون نظارت برای کشف انومالی می باشد در این jupyter notebook این الگوریتم بر روی ۹ میلیون تراکنش مربوط به جیرینگ اعمال شده است دیتای مربوط به تراکنش ها در دو دسته زیر قابل دانلود است:
داده های نمونه: https://ufile.io/4sv1ugpt
کل مجموعه داده ها:
https://ufile.io/4sv1ugpt
تشکر از خانم معارفیبرای مجموعه داده ها
@Machine_learn
# Histogram-based Outlier Score (HBOS): A fastUnsupervised Anomaly Detection Algorithm #Paper #HBOS #Anomaly_Detection @Machine_learn
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
A new open benchmark for speech recognition with limited or no supervision
https://ai.facebook.com/blog/a-new-open-benchmark-for-speech-recognition-with-limited-or-no-supervision/
Code and dataset: https://ai.facebook.com/tools/libri-light
Full paper: https://arxiv.org/abs/1912.07875
T5: Text-To-Text Transfer Transformer
Github: https://github.com/google-research/text-to-text-transfer-transformer
Paper: https://arxiv.org/abs/1910.10683
@Machine_learn
A collection of anomaly detection methods #Code #Python #Anomaly_detection @Machine_learn
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
https://arxiv.org/abs/1910.06922
SIte : https://ajolicoeur.wordpress.com/
Github : https://github.com/AlexiaJM/MaximumMarginGANs
Practical Machine Learning with Python
#ML #Python
@Machine_learn
GNNExplainer: Generating Explanations for Graph Neural Networks
https://arxiv.org/abs/1903.03894
Github : https://github.com/RexYing/gnn-model-explainer/
New book 🔥DEEP LEARNING WITH PYTORCH 2019 #DL #Python
#Book #CNN #RNN
@Machine_learn
👌Finding label errors in datasets and learning with noisy labels.
https://github.com/cgnorthcutt/cleanlab/
