LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving
🖥 Github: https://github.com/OpenDriveLab/LaneSegNet
📕 Paper: https://arxiv.org/abs/2312.16108v1
🔥Datasets: https://paperswithcode.com/dataset/openlane-v2
@Machine_learn
🖥 Github: https://github.com/OpenDriveLab/LaneSegNet
📕 Paper: https://arxiv.org/abs/2312.16108v1
🔥Datasets: https://paperswithcode.com/dataset/openlane-v2
@Machine_learn
🧠 EasyVolcap: Accelerating Neural Volumetric Video Research
🖥 Code: https://github.com/zju3dv/easyvolcap
🖌Metrics: https://short.llm360.ai/amber-metrics
📚 Paper: https://arxiv.org/abs/2312.06575v1
⚡️ Dataset: https://paperswithcode.com/dataset/nerf
@Machine_learn
🖥 Code: https://github.com/zju3dv/easyvolcap
🖌Metrics: https://short.llm360.ai/amber-metrics
📚 Paper: https://arxiv.org/abs/2312.06575v1
⚡️ Dataset: https://paperswithcode.com/dataset/nerf
@Machine_learn
TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming
🖥 Github: https://github.com/IITB-LEAP-OCR/TEXTRON
📕 Paper: https://openaccess.thecvf.com/content/WACV2024/papers/Kudale_Textron_Weakly_Supervised_Multilingual_Text_Detection_Through_Data_Programming_WACV_2024_paper.pdf
🔥Datasets: https://paperswithcode.com/dataset/docbank
✨ Tasks: https://paperswithcode.com/task/optical-character-recognition
@Machine_learn
🖥 Github: https://github.com/IITB-LEAP-OCR/TEXTRON
📕 Paper: https://openaccess.thecvf.com/content/WACV2024/papers/Kudale_Textron_Weakly_Supervised_Multilingual_Text_Detection_Through_Data_Programming_WACV_2024_paper.pdf
🔥Datasets: https://paperswithcode.com/dataset/docbank
✨ Tasks: https://paperswithcode.com/task/optical-character-recognition
@Machine_learn
TinyLlama-1.1B
🖥 Github: https://github.com/jzhang38/tinyllama
📕 Paper: https://arxiv.org/pdf/2401.02385v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/mmlu
@Machine_learn
🖥 Github: https://github.com/jzhang38/tinyllama
📕 Paper: https://arxiv.org/pdf/2401.02385v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/mmlu
@Machine_learn
💡 TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering
Выпущен TextDiffuser-2 с кодом и демо.
📚 Paper: https://arxiv.org/abs/2311.16465
🖥 Code: https://github.com/microsoft/unilm/tree/master/textdiffuser-2
⚡️ Demo: https://huggingface.co/spaces/JingyeChen22/TextDiffuser-2
@Machine_learn
Выпущен TextDiffuser-2 с кодом и демо.
📚 Paper: https://arxiv.org/abs/2311.16465
🖥 Code: https://github.com/microsoft/unilm/tree/master/textdiffuser-2
⚡️ Demo: https://huggingface.co/spaces/JingyeChen22/TextDiffuser-2
@Machine_learn
Sign Language Understanding
🖥 Github: https://github.com/FangyunWei/SLRT
📕 Paper: https://arxiv.org/pdf/2401.05336v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/csl-daily
@Machine_learn
🖥 Github: https://github.com/FangyunWei/SLRT
📕 Paper: https://arxiv.org/pdf/2401.05336v1.pdf
🔥Datasets: https://paperswithcode.com/dataset/csl-daily
@Machine_learn
Fight Fraud with Machine Learning.pdf
32 MB
Book: Fight Fraud with Machine Learning
Authors: Ashish Ranjan Jha
ISBN: Null
year: 2023
pages: 288
Tags: #Machine_learning
@Machine_learn
Authors: Ashish Ranjan Jha
ISBN: Null
year: 2023
pages: 288
Tags: #Machine_learning
@Machine_learn
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🖼 AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
🖥 Github: https://github.com/guoyww/animatediff/
💻 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb
📚 Paper: https://arxiv.org/abs/2307.04725
📁 Project: https://animatediff.github.io/
@Machine_learn
🖥 Github: https://github.com/guoyww/animatediff/
💻 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb
📚 Paper: https://arxiv.org/abs/2307.04725
📁 Project: https://animatediff.github.io/
@Machine_learn
Language Tool
LanguageTool is an Open Source proofreading software for English, Spanish, French, German, Portuguese, Polish, Dutch, and more than 20 other languages. It finds many errors that a simple spell checker cannot detect.
Creator: LanguageTool
Stars: ⭐️ 9.8k
Forked by: 1.1k
GitHub repo: https://github.com/languagetool-org/languagetool
#tools #LanguageTools
➖➖➖➖➖➖➖➖➖➖
@Machine_learn
LanguageTool is an Open Source proofreading software for English, Spanish, French, German, Portuguese, Polish, Dutch, and more than 20 other languages. It finds many errors that a simple spell checker cannot detect.
Creator: LanguageTool
Stars: ⭐️ 9.8k
Forked by: 1.1k
GitHub repo: https://github.com/languagetool-org/languagetool
#tools #LanguageTools
➖➖➖➖➖➖➖➖➖➖
@Machine_learn
GitHub
GitHub - languagetool-org/languagetool: Style and Grammar Checker for 25+ Languages
Style and Grammar Checker for 25+ Languages. Contribute to languagetool-org/languagetool development by creating an account on GitHub.
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
🖥 Github: https://github.com/kaistai/prometheus-vision
📕 Paper: https://arxiv.org/abs/2401.06591v1
🔥Datasets: https://paperswithcode.com/dataset/ok-vqa
@Machine_learn
🖥 Github: https://github.com/kaistai/prometheus-vision
📕 Paper: https://arxiv.org/abs/2401.06591v1
🔥Datasets: https://paperswithcode.com/dataset/ok-vqa
@Machine_learn
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🖼 ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation
🖥 GitHub: https://github.com/bytedance/ImageDream
📚 Paper: https://arxiv.org/abs/2312.02201
🥩 Demo: https://github.com/bytedance/ImageDream/blob/main
@Machine_learn
🖥 GitHub: https://github.com/bytedance/ImageDream
📚 Paper: https://arxiv.org/abs/2312.02201
🥩 Demo: https://github.com/bytedance/ImageDream/blob/main
@Machine_learn
SAM-Med2D ➕
SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
🖥 Github: https://github.com/uni-medical/sam-med2d
🖥 Colab: https://colab.research.google.com/github/uni-medical/SAM-Med2D/blob/main/predictor_example.ipynb
📕 Paper: https://arxiv.org/abs/2308.16184
⭐️ Dataset: https://paperswithcode.com/dataset/sa-1b
@Machine_learn
SAM-Med2D, the most comprehensive studies on applying SAM to medical 2D images.
🖥 Github: https://github.com/uni-medical/sam-med2d
🖥 Colab: https://colab.research.google.com/github/uni-medical/SAM-Med2D/blob/main/predictor_example.ipynb
📕 Paper: https://arxiv.org/abs/2308.16184
⭐️ Dataset: https://paperswithcode.com/dataset/sa-1b
@Machine_learn
Machine_Learning_For_Financial_Risk_Management_With_Python_Algorithms.pdf
3.6 MB
Book: Machine Learning for Financial
Risk Management with Python
Authors: Abdullah Karasan
ISBN: 978-1-492-08518-8
year: 2022
pages: 194
Tags: #Machine_learning #financial
@Machine_learn
Risk Management with Python
Authors: Abdullah Karasan
ISBN: 978-1-492-08518-8
year: 2022
pages: 194
Tags: #Machine_learning #financial
@Machine_learn
Forwarded from Machine learning books and papers
با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
GraphEmb
🖥 Github: https://github.com/ubioinformat/graphemb
📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf
✨ Tasks: https://paperswithcode.com/task/benchmarking
@Machine_learn
🖥 Github: https://github.com/ubioinformat/graphemb
📕 Paper: https://arxiv.org/pdf/2311.12670v1.pdf
✨ Tasks: https://paperswithcode.com/task/benchmarking
@Machine_learn
2306.08302.pdf
3.2 MB
paper: Unifying Large Language Models and Knowledge Graphs: A Roadmap
index: Natural Language Processing, Large Language Models, Generative Pre-Training, Knowledge Graphs, Roadmap, Bidirectional Reasoning.
@Machine_learn
index: Natural Language Processing, Large Language Models, Generative Pre-Training, Knowledge Graphs, Roadmap, Bidirectional Reasoning.
@Machine_learn
با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa
1: introduction to machine learning
2: Regression (linear and non-linear)
3: Tensorflow introduction
4: Tensorflow computaion graph
5: Tensorflow optimizer and loss function
6: Tensorflow linear and non linear regression
7: logistic regression
8: Tensorflow regression
___________
9: introduction to traditional machine learning
*10: knn and desicion tree
*11: desicion tree and Naive bayes
*12: desicion tree, knn, Naive bayes implementation
*13: k-means
*14: Guassion Mixture Model(GMM)
*15: implementation K-means and GMM
_
16: introduction to Artificial Neural Network
17: Multi-level Neural Network
18: Introduction to Convolution Neural Network
19: Tensorflow Multi-level Neural Network
20:Tensorflow CNN
21:CNN image clasaification
22: Cnn text clasaification
23: Recurrent Neural Network(RNN)
جهت تهیه می تونین به ایدی بنده مراجعه کنین
@Raminmousa