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đĒ Making a web app generator with open ML models
đĨ Github: https://github.com/huggingface/blog/blob/main/text-to-webapp.md
đ HuggingFace: https://huggingface.co/blog/text-to-webapp
đDemo: https://huggingface.co/spaces/jbilcke-hf/webapp-factory-wizardcoder
https://t.iss.one/DataScienceT
đĨ Github: https://github.com/huggingface/blog/blob/main/text-to-webapp.md
đ HuggingFace: https://huggingface.co/blog/text-to-webapp
đDemo: https://huggingface.co/spaces/jbilcke-hf/webapp-factory-wizardcoder
https://t.iss.one/DataScienceT
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đ¤ŗFiltered-Guided Diffusion
đĨ Github: https://github.com/jaclyngu/filteredguideddiffusion
⊠Paper: https://arxiv.org/pdf/2306.17141v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/afhq
https://t.iss.one/DataScienceT
đĨ Github: https://github.com/jaclyngu/filteredguideddiffusion
⊠Paper: https://arxiv.org/pdf/2306.17141v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/afhq
https://t.iss.one/DataScienceT
â¤âđĨ1â¤1đ1
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đĒŠ DISCO: Human Dance Generation
âī¸ NTU (+ #Microsoft) unveils DISCO: a big step towards the Human Dance Generation.
đ Review https://t.ly/cNGX
đ Paper arxiv.org/pdf/2307.00040.pdf
đProject: disco-dance.github.io/
đ Code github.com/Wangt-CN/DisCo
https://t.iss.one/DataScienceT
âī¸ NTU (+ #Microsoft) unveils DISCO: a big step towards the Human Dance Generation.
đ Review https://t.ly/cNGX
đ Paper arxiv.org/pdf/2307.00040.pdf
đProject: disco-dance.github.io/
đ Code github.com/Wangt-CN/DisCo
https://t.iss.one/DataScienceT
đ3â¤1
Building an Image Recognition API using Flask.
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
âĸ An example of using curl (image 5.)
âĸ An example using Python queries (image 6.)
https://t.iss.one/DataScienceT
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
âĸ An example of using curl (image 5.)
âĸ An example using Python queries (image 6.)
https://t.iss.one/DataScienceT
â¤âđĨ2đ2đ1
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đ Hierarchical Open-vocabulary Universal Image Segmentation
Decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".
đĨ Github: https://github.com/berkeley-hipie/hipie
đ Paper: https://arxiv.org/abs/2307.00764v1
đProject: https://people.eecs.berkeley.edu/~xdwang/projects/HIPIE/
đ Dataset: https://paperswithcode.com/dataset/pascal-panoptic-parts
https://t.iss.one/DataScienceT
Decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".
đĨ Github: https://github.com/berkeley-hipie/hipie
đ Paper: https://arxiv.org/abs/2307.00764v1
đProject: https://people.eecs.berkeley.edu/~xdwang/projects/HIPIE/
đ Dataset: https://paperswithcode.com/dataset/pascal-panoptic-parts
https://t.iss.one/DataScienceT
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đFoundation Model for Endoscopy Video Analysis
đĨ Github: https://github.com/med-air/endo-fm
⊠Paper: https://arxiv.org/pdf/2306.16741v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/kumc
https://t.iss.one/DataScienceT
đĨ Github: https://github.com/med-air/endo-fm
⊠Paper: https://arxiv.org/pdf/2306.16741v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/kumc
https://t.iss.one/DataScienceT
â¤âđĨ3
We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free
To request a subscription: talk to @Hussein_Sheikho
To request a subscription: talk to @Hussein_Sheikho
â¤3đ3
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đ¨ Making ML-powered web games with Transformers.js
The goal of this tutorial is to show you how easy it is to make your own ML-powered web game.
đĨ Github: https://github.com/xenova/doodle-dash
đ¤ Hugging face: https://huggingface.co/blog/ml-web-games
âī¸ Code: https://github.com/xenova/doodle-dash
đDemo: https://huggingface.co/spaces/Xenova/doodle-dash
đ Dataset: https://huggingface.co/datasets/Xenova/quickdraw-small
https://t.iss.one/DataScienceT
The goal of this tutorial is to show you how easy it is to make your own ML-powered web game.
đĨ Github: https://github.com/xenova/doodle-dash
đ¤ Hugging face: https://huggingface.co/blog/ml-web-games
âī¸ Code: https://github.com/xenova/doodle-dash
đDemo: https://huggingface.co/spaces/Xenova/doodle-dash
đ Dataset: https://huggingface.co/datasets/Xenova/quickdraw-small
https://t.iss.one/DataScienceT
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đĻ Focused Transformer: Contrastive Training for Context Scaling
LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more.
đĨ Github: https://github.com/cstankonrad/long_llama
đ Paper: https://arxiv.org/abs/2307.03170v1
đĨ Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
đ Dataset: https://paperswithcode.com/dataset/pg-19
https://t.iss.one/DataScienceT
LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more.
đĨ Github: https://github.com/cstankonrad/long_llama
đ Paper: https://arxiv.org/abs/2307.03170v1
đĨ Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
đ Dataset: https://paperswithcode.com/dataset/pg-19
https://t.iss.one/DataScienceT
đ3â¤1â¤âđĨ1
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ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation âģī¸
đĨ Github: https://github.com/pendu/containergym
⊠Paper: https://arxiv.org/pdf/2307.02991v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
đĨ Github: https://github.com/pendu/containergym
⊠Paper: https://arxiv.org/pdf/2307.02991v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
đ1
đĨ Chat Downloader
A simple tool used to retrieve chat messages from livestreams, videos, clips and past broadcasts.
- YouTube.com
- Zoom.us
- Facebook.com
- Twitch.tv
đĨ Github
đ Docs
https://t.iss.one/DataScienceT
A simple tool used to retrieve chat messages from livestreams, videos, clips and past broadcasts.
- YouTube.com
- Zoom.us
- Facebook.com
- Twitch.tv
$ pip install chat-downloader
Using:
# termimal
$ chat_downloader https://www.youtube.com/watch?v=video_link --output chat.json
# Python script
from chat_downloader import ChatDownloader
url = 'https://www.youtube.com/watch?v=video_link'
chat = ChatDownloader().get_chat(url)
for message in chat:
chat.print_formatted(message)
đĨ Github
đ Docs
https://t.iss.one/DataScienceT
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đĨ Tkinter Designer
An easy and fast way to create a Python GUI đ
đĨ Github
https://t.iss.one/DataScienceT
An easy and fast way to create a Python GUI đ
đĨ Github
https://t.iss.one/DataScienceT
đ6â¤2
Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification
đĨ Github: https://github.com/yuyongcan/benchmark-tta
⊠Paper: https://arxiv.org/pdf/2307.03133v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
đĨ Github: https://github.com/yuyongcan/benchmark-tta
⊠Paper: https://arxiv.org/pdf/2307.03133v1.pdf
đ¨ Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
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đ DeepOnto: A Python Package for Ontology Engineering with Deep Learning
A package for ontology engineering with deep learning and language model.
pip install deeponto
đĨ Github: https://github.com/KRR-Oxford/DeepOnto
đ Project: https://krr-oxford.github.io/DeepOnto/
đ Paper: https://arxiv.org/abs/2307.03067v1
đ Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
A package for ontology engineering with deep learning and language model.
pip install deeponto
đĨ Github: https://github.com/KRR-Oxford/DeepOnto
đ Project: https://krr-oxford.github.io/DeepOnto/
đ Paper: https://arxiv.org/abs/2307.03067v1
đ Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
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