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
@Machine_learn
Release 0.5.beta1
Authors: Kushal Das
ISBN: Null
year: 2023
pages: 175
Tags: #Python #Code
@Machine_learn
❤5
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
@Machine_learn
🖥 Github: https://github.com/wuyxin/disc
⏩ Paper: https://arxiv.org/pdf/2305.00650v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/metashift
@Machine_learn
👍3
Segment Any Anomaly without Training via Hybrid Prompt Regularization
🖥 Github: https://github.com/caoyunkang/segment-any-anomaly
🖥 Colab: https://colab.research.google.com/drive/1Rwio_KfziuLp79Qh_ugum64Hjnq4ZwsE?usp=sharing
⏩ Paper: https://arxiv.org/abs/2305.11013v1
📌 Dataset: https://paperswithcode.com/dataset/visa
@Machine_learn
🖥 Github: https://github.com/caoyunkang/segment-any-anomaly
🖥 Colab: https://colab.research.google.com/drive/1Rwio_KfziuLp79Qh_ugum64Hjnq4ZwsE?usp=sharing
⏩ Paper: https://arxiv.org/abs/2305.11013v1
📌 Dataset: https://paperswithcode.com/dataset/visa
@Machine_learn
👍2
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
@Machine_learn
Authors: Palash Goyal, Sumit Pandey, Karan Jain
ISBN: 978-1-4842-3685-7
year: 2018
pages: 290
Tags: #NLP #DL #Python #Code
@Machine_learn
👍6🔥2
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
@Machine_learn
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
@Machine_learn
👍4
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
@Machine_learn
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
@Machine_learn
👍3
Hybrid and Collaborative Passage Reranking
🖥 Github: https://github.com/zmzhang2000/hybrank
⏩ Paper: https://arxiv.org/pdf/2305.09313v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/natural-questions
@Machine_learn
🖥 Github: https://github.com/zmzhang2000/hybrank
⏩ Paper: https://arxiv.org/pdf/2305.09313v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/natural-questions
@Machine_learn
👍3🔥1
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.
🖥 Github: https://github.com/facebookresearch/hiera
⏩ Paper: https://arxiv.org/abs/2306.00989v1
📌 Dataset: https://paperswithcode.com/dataset/inaturalist
@Machine_learn
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
@Machine_learn
👍4
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.
@Raminmousa
@Raminmousa
🔥2👍1
🦍 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
@Machine_learn
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
@Machine_learn
👍5
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
@Machine_learn
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
@Machine_learn
👍2
TabEAE
🖥 Github: https://github.com/stardust-hyx/tabeae
⏩ Paper: https://arxiv.org/pdf/2306.00502v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/wikievents
@Machine_learn
🖥 Github: https://github.com/stardust-hyx/tabeae
⏩ Paper: https://arxiv.org/pdf/2306.00502v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/wikievents
@Machine_learn
👍4❤1
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
@Machine_learn
GUIDE ACE-PREP
Authors: null
ISBN: 978-1-915002-10-5
year: 2022
pages: 136
Tags: #Data_Science
@Machine_learn
👍9
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
@Machine_learn
🖥 Github: https://github.com/pietroastolfi/suave-daino
⏩ Paper: https://arxiv.org/pdf/2306.07483v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/imagenet
@Machine_learn
👍5❤1🔥1
https://www.globaldevelopment.dk/media/attachments/2021/07/31/practical-machine-learning-and-image-processing-1st-edition.pdf
Book: practical machine learning and image processing
year: 2019
Tags: #Data_Science #ML
@Machine_learn
Book: practical machine learning and image processing
year: 2019
Tags: #Data_Science #ML
@Machine_learn
🐼 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
@Machine_learn
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
@Machine_learn
❤4👍1🔥1
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
@Machine_learn
🖥 Github: https://github.com/efficienttraining/labelbench
⏩ Paper: https://arxiv.org/pdf/2306.09910v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/cifar-10
@Machine_learn
👍2❤1