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🎨 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.
🖥 Github: https://github.com/xenova/doodle-dash
🤗 Hugging face: https://huggingface.co/blog/ml-web-games
⭐️ Code: https://github.com/xenova/doodle-dash
🔗Demo: https://huggingface.co/spaces/Xenova/doodle-dash
🔗 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.
🖥 Github: https://github.com/xenova/doodle-dash
🤗 Hugging face: https://huggingface.co/blog/ml-web-games
⭐️ Code: https://github.com/xenova/doodle-dash
🔗Demo: https://huggingface.co/spaces/Xenova/doodle-dash
🔗 Dataset: https://huggingface.co/datasets/Xenova/quickdraw-small
https://t.iss.one/DataScienceT
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🦙 Focused Transformer: Contrastive Training for Context Scaling
LongLLaMA, a large language model capable of handling long contexts of 256k tokens or even more.
🖥 Github: https://github.com/cstankonrad/long_llama
📕 Paper: https://arxiv.org/abs/2307.03170v1
🖥 Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
🔗 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.
🖥 Github: https://github.com/cstankonrad/long_llama
📕 Paper: https://arxiv.org/abs/2307.03170v1
🖥 Colab: https://colab.research.google.com/github/CStanKonrad/long_llama/blob/main/long_llama_colab.ipynb
🔗 Dataset: https://paperswithcode.com/dataset/pg-19
https://t.iss.one/DataScienceT
👍3❤1❤🔥1
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ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation ♻️
🖥 Github: https://github.com/pendu/containergym
⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/pendu/containergym
⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/openai-gym
https://t.iss.one/DataScienceT
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🖥 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
🖥 Github
📝 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) 🖥 Github
📝 Docs
https://t.iss.one/DataScienceT
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🖥 Tkinter Designer
An easy and fast way to create a Python GUI 🐍
🖥 Github
https://t.iss.one/DataScienceT
An easy and fast way to create a Python GUI 🐍
🖥 Github
https://t.iss.one/DataScienceT
👍6❤2
Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification
🖥 Github: https://github.com/yuyongcan/benchmark-tta
⏩ Paper: https://arxiv.org/pdf/2307.03133v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/yuyongcan/benchmark-tta
⏩ Paper: https://arxiv.org/pdf/2307.03133v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/imagenet
https://t.iss.one/DataScienceT
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🔎 DeepOnto: A Python Package for Ontology Engineering with Deep Learning
A package for ontology engineering with deep learning and language model.
pip install deeponto
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 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
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
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Top 6 Algorithms Every Software Engineer Should Know
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
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⭐️ InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval.
🖥 Github: https://github.com/zetaalphavector/inpars
📕 Paper: https://arxiv.org/abs/2307.04601v1
🚀 Dataset: https://paperswithcode.com/dataset/beir
https://t.iss.one/DataScienceT
pip install inpars🖥 Github: https://github.com/zetaalphavector/inpars
📕 Paper: https://arxiv.org/abs/2307.04601v1
🚀 Dataset: https://paperswithcode.com/dataset/beir
https://t.iss.one/DataScienceT
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Django Roadmap
Link 1: https://github.com/HHHMHA/django-roadmap
Link 2:
https://github.com/faresemad/Django-Roadmap
Share this roadmap for your friends
https://t.iss.one/CodeProgrammer
Link 1: https://github.com/HHHMHA/django-roadmap
Link 2:
https://github.com/faresemad/Django-Roadmap
Share this roadmap for your friends
https://t.iss.one/CodeProgrammer
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Fourier-Net
🖥 Github: https://github.com/xi-jia/fourier-net
⏩ Paper: https://arxiv.org/pdf/2307.02997v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/learn2reg
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/xi-jia/fourier-net
⏩ Paper: https://arxiv.org/pdf/2307.02997v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/learn2reg
https://t.iss.one/DataScienceT
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🔥 Generative Pretraining in Multimodality
Model can take in any single-modality or multimodal data input indiscriminately through a one-model-for-all autoregressive training process.
🖥 Github: https://github.com/baaivision/emu
📕 Paper: https://arxiv.org/abs/2307.05222v1
🚀 Dataset: https://paperswithcode.com/dataset/mmc4
https://t.iss.one/DataScienceT
Model can take in any single-modality or multimodal data input indiscriminately through a one-model-for-all autoregressive training process.
🖥 Github: https://github.com/baaivision/emu
📕 Paper: https://arxiv.org/abs/2307.05222v1
🚀 Dataset: https://paperswithcode.com/dataset/mmc4
https://t.iss.one/DataScienceT
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