Data Science | Machine Learning with Python for Researchers
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Data Science | Machine Learning with Python for Researchers
31.5K subscribers
Data Science | Machine Learning with Python for Researchers
🧑‍💻DeciCoder: A new open-source LLM, specialized for generating code in Python, Java, and Javascript..

- parameters: 1 B
- dataset: 'The Stack' dataset
- supports: Python, Javascript, Java
- context: 2048 tokens

▪Model
▪Colab
▪Dataset

https://t.iss.one/DataScienceT
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Data Science | Machine Learning with Python for Researchers
✔️ DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching

🖥 Github: https://github.com/parskatt/dedode

☑️ TensorRT: https://github.com/fabio-sim/DeDoDe-ONNX-TensorRT

📕 Paper: https://arxiv.org/abs/2308.08479

⭐️ Demos: https://github.com/Parskatt/DeDoDe/blob/main/demo

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Data Science | Machine Learning with Python for Researchers
Forwarded from Python | Machine Learning | Coding | R
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💨CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

🖥 Github: https://github.com/qiuyu96/codef

☑️ Project: https://qiuyu96.github.io/CoDeF/

📕 Paper: https://arxiv.org/abs/2308.07926

⭐️ Demo: https://ezioby.github.io/CoDeF_Demo/

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Data Science | Machine Learning with Python for Researchers
EQ-Net: Elastic Quantization Neural Networks

🖥 Github: https://github.com/xuke225/eq-net

📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/imagenet

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Data Science | Machine Learning with Python for Researchers
☄️Dataset Quantization

DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.

git clone https://github.com/vimar-gu/DQ.git
cd DQ

🖥 Github: https://github.com/magic-research/dataset_quantization

📕 Paper: https://arxiv.org/abs/2308.10524v1

☑️ Dataset: https://paperswithcode.com/dataset/gsm8k

https://t.iss.one/DataScienceT
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Data Science | Machine Learning with Python for Researchers
Forwarded from Data Science Books
Machine Learning for Data Science Handbook (2023)

This book is available now only in paid channel

Pages: 975 pages
Rate: ⭐️⭐️⭐️⭐️⭐️

Cost of subscription in Paid channel is 5$ for one time and forever

Channel link: https://t.iss.one/+LnCmAFJO3tNmYjUy

Paid channel contain important book and udemy and other courses as zip files

Welcome all
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Data Science | Machine Learning with Python for Researchers
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

🖥 Github: https://github.com/osvai/ske2grid

📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ucf101

https://t.iss.one/DataScienceT
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Data Science | Machine Learning with Python for Researchers
EQ-Net: Elastic Quantization Neural Networks

🖥 Github: https://github.com/xuke225/eq-net

📕 Paper: https://arxiv.org/pdf/2308.07650v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/imagenet

https://t.iss.one/DataScienceT
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Data Science | Machine Learning with Python for Researchers
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🏅MixSort

MixSort is the proposed baseline tracker in SportMOT.

🖥 Github: https://github.com/MCG-NJU/MixSort

📕 Paper: https://arxiv.org/pdf/2304.05170.pdf

⭐️ SportsMOT: https://github.com/MCG-NJU/SportsMOT

https://t.iss.one/DataScienceT
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Data Science | Machine Learning with Python for Researchers
⚡prompt2model - Generate Deployable Models from Instructions

prompt2model - Generate Deployable Models from Natural Language Instructions

pip install prompt2model

🖥 Github: https://github.com/neulab/prompt2model

📕 Paper: https://arxiv.org/abs/2308.12261v1

⭐️ Demo: https://github.com/facebookresearch/sonar#usage

☑️ Dataset: https://paperswithcode.com/dataset/mconala

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Data Science | Machine Learning with Python for Researchers
🔥Dense Text-to-Image Generation with Attention Modulation

DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle dense captions while offering control over the scene layout.

🖥 Github: https://github.com/naver-ai/densediffusion

📕 Paper: https://arxiv.org/abs/2308.12964v1

⭐️ Dataset: https://paperswithcode.com/dataset/coco

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Data Science | Machine Learning with Python for Researchers
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

🖥 Github: https://github.com/llvy21/duic

📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pixel-art

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Data Science | Machine Learning with Python for Researchers
LibCity

🖥 Github: https://github.com/libcity/bigscity-libcity

📕 Paper: https://arxiv.org/pdf/2308.12899v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/taxibj

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Data Science | Machine Learning with Python for Researchers
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Data Science | Machine Learning with Python for Researchers
S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

🖥 Github: https://github.com/sheng-eatamath/s3a

📕 Paper: https://arxiv.org/pdf/2308.12960v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-100

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Data Science | Machine Learning with Python for Researchers
Forwarded from Python | Machine Learning | Coding | R
🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python

❯ https://scaler.com/topics/course/python-for-beginners/

❯ https://simplilearn.com/learn-python-basics-free-course-skillup

3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python

4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/

5. Take it to next level

❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/

❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django

❯ Machine Learning
https://developers.google.com/machine-learning/crash-course

❯ Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning

https://t.iss.one/CodeProgrammer

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