Machine learning books and papers
Admin: @Raminmousa1 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 24 474 subscribers, ranking 8 097 in the Education category and 14 071 in the Iran region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 474 subscribers.
According to the latest data from 26 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -40 over the last 30 days and by -2 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 6.38%. Within the first 24 hours after publication, content typically collects 2.32% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 561 views. Within the first day, a publication typically gains 568 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- 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: @Raminmousa1
ID: @Machine_learn
link: https://t.me/Machine_learn”
Thanks to the high frequency of updates (latest data received on 27 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.
# setting up a venv:
conda create -n depth-pro -y python=3.9
conda activate depth-pro
pip install -e .
# Download pretrained checkpoints:
source get_pretrained_models.sh
# Run the inference from CLI on a single image:
depth-pro-run -i ./data/example.jpg
# Running from python
from PIL import Image
import depth_pro
model, transform = depth_pro.create_model_and_transforms()
model.eval()
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)
prediction = model.infer(image, f_px=f_px)
depth = prediction["depth"] # Depth in [m].
focallength_px = prediction["focallength_px"] # Focal length in pixels.
🟡Demo
🟡Arxiv
🖥GitHub
@Machine_learn
