Machine learning books and papers
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Admin: @Raminmousa
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ID: @Machine_learn
link: https://t.me/Machine_learn
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pymbook.pdf
1.1 MB
Book: Python for you and me
Release 0.5.beta1
Authors: Kushal Das
ISBN: Null
year: 2023
pages: 175
Tags: #Python #Code
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Discover and Cure: Concept-aware Mitigation of Spurious Correlation

🖥 Github: https://github.com/wuyxin/disc

Paper: https://arxiv.org/pdf/2305.00650v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/metashift

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Deep-Learning-for-Natural-Language-Processing.pdf
7.3 MB
Book: Deep Learning for Natural Language Processing (Creating Neural Networks with Python)
Authors: Palash Goyal, Sumit Pandey, Karan Jain
ISBN: 978-1-4842-3685-7
year: 2018
pages: 290
Tags: #NLP #DL #Python #Code
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LLM-Pruner: On the Structural Pruning of Large Language Models

Compress your LLMs to any size;


🖥 Github: https://github.com/horseee/llm-pruner

Paper: https://arxiv.org/abs/2305.11627v1

📌 Dataset: https://paperswithcode.com/dataset/piqa

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QLoRA: Efficient Finetuning of Quantized LLMs

Model name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.


🖥 Github: https://github.com/artidoro/qlora

Paper: https://arxiv.org/abs/2305.14314

⭐️ Demo: https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi

📌 Dataset: https://paperswithcode.com/dataset/ffhq

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Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

Hiera is a hierarchical vision transformer that is fast, powerful, and, above all, simple. It outperforms the state-of-the-art across a wide array of image and video tasks while being much faster.

pip install hiera-transformer

🖥 Github: https://github.com/facebookresearch/hiera

Paper: https://arxiv.org/abs/2306.00989v1

📌 Dataset: https://paperswithcode.com/dataset/inaturalist

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25_Awesome_Python_Scripts.pdf
171.4 KB
A Collection of 25 Awesome Python Scripts (mini projects)
#Python #Mini_Projects
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با عرض سلام پکیچ های یادگیری ماشین و یادگیری عمیق رو برای دوستانی که نیاز دارن تخفیف۵۰٪ گذاشتیم در صورت نیاز به بنده اطلاع بدین.
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🦍 Gorilla: Large Language Model Connected with Massive APIs

Gorilla a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls.


🖥 Github: https://github.com/ShishirPatil/gorilla

📕 Paper: https://arxiv.org/abs/2305.15334

🔗 Demo: https://drive.google.com/file/d/1E0k5mG1mTiaz0kukyK1PdeohJipTFh6j/view?usp=share_link

👉 Project: https://shishirpatil.github.io/gorilla/

⭐️ Colab: https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing

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Segment Anything 3D

SAM-3D: A toolbox transfers 2D SAM segments into 3D scene-level point clouds.

🖥 Github: https://github.com/pointcept/segmentanything3d

Paper: https://arxiv.org/abs/2306.03908v1

📌 Dataset: https://paperswithcode.com/dataset/scannet
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python-regular-expressions-cheat-sheet.pdf
49 KB
Data Science Cheat Sheet
Python Regular Expressions

#Python
#RE
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Data Science Interview (en).pdf
849.5 KB
Book: DATA SCIENCE INTERVIEW
GUIDE ACE-PREP
Authors: null
ISBN: 978-1-915002-10-5
year: 2022
pages: 136
Tags: #Data_Science
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Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]

🖥 Github: https://github.com/pietroastolfi/suave-daino

Paper: https://arxiv.org/pdf/2306.07483v1.pdf

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

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🐼 PandaLM: ReProducible and Automated Language Model Assessment

Judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets.

🖥 Github: https://github.com/weopenml/pandalm

📕 Paper: https://arxiv.org/abs/2306.05087v1

🔗 Dataset: https://github.com/tatsu-lab/stanford_alpaca#data-release

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LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning

🖥 Github: https://github.com/efficienttraining/labelbench

Paper: https://arxiv.org/pdf/2306.09910v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/cifar-10

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