β‘οΈ MVTN: Learning Multi-View Transformations for 3D Understanding
π₯Github: https://github.com/ajhamdi/mvtorch
βοΈ Paper: https://arxiv.org/abs/2212.13462v1
β© Dataset: https://paperswithcode.com/dataset/modelnet
β© Π‘lassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb
β‘οΈ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb
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π₯Github: https://github.com/ajhamdi/mvtorch
βοΈ Paper: https://arxiv.org/abs/2212.13462v1
β© Dataset: https://paperswithcode.com/dataset/modelnet
β© Π‘lassification example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/classification.ipynb
β‘οΈ Segmentation example: https://github.com/ajhamdi/mvtorch/blob/main/docs/tutorials/segmentation.ipynb
@Machine_learn
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When you are presenting a topic in the class and make eye contact with your friendsπΉπΉπΉ
@Machine_learn
@Machine_learn
π2π1
βοΈ The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
π₯ Github: https://paperswithcode.com/paper/the-cropandweed-dataset-a-multi-modal
β© Paper: https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html
β‘οΈ Datasets: https://paperswithcode.com/dataset/cropandweed-dataset
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π₯ Github: https://paperswithcode.com/paper/the-cropandweed-dataset-a-multi-modal
β© Paper: https://openaccess.thecvf.com/content/WACV2023/html/Steininger_The_CropAndWeed_Dataset_A_Multi-Modal_Learning_Approach_for_Efficient_Crop_WACV_2023_paper.html
β‘οΈ Datasets: https://paperswithcode.com/dataset/cropandweed-dataset
@Machine_learn
π4
Math-for-Programmers.pdf
27.7 MB
MEAP Edition
Manning Early Access Program
Math for Programmers
3D graphics, machine learning, and simulations with Python
Version 11
#book @Machine_learn
Manning Early Access Program
Math for Programmers
3D graphics, machine learning, and simulations with Python
Version 11
#book @Machine_learn
π6π5
book.pdf
52.1 MB
π8β€2
Build_a_Career_in_Data_Science_by_Emily_Robinson,_Jacqueline_Nolis.pdf
12.3 MB
Build a Career in Data Science
EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
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EMILY ROBINSON AND JACQUELINE NOLIS
#Data_Science
#Book
#ML
@Machine_learn
π1
π¬ GLIGEN: Open-Set Grounded Text-to-Image Generation
GLIGENβs zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
βοΈ Project: https://gligen.github.io/
βοΈ Demo: https://aka.ms/gligen
β οΈ Paper: https://arxiv.org/abs/2301.07093
π₯ Github: https://github.com/gligen/GLIGEN
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GLIGENβs zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.
βοΈ Project: https://gligen.github.io/
βοΈ Demo: https://aka.ms/gligen
β οΈ Paper: https://arxiv.org/abs/2301.07093
π₯ Github: https://github.com/gligen/GLIGEN
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Apress.PyTorch.pdf
5.1 MB
PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models, 2nd Edition (2022)
#Pythorch #book #python
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#Pythorch #book #python
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π₯1
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AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
Autoregressive approach for modeling dynamically deforming human bodies by Meta.
π₯ Github: github.com/facebookresearch/AutoAvatar
βοΈ Project: zqbai-jeremy.github.io/autoavatar
β οΈ Paprer: arxiv.org/pdf/2203.13817.pdf
β© Dataset: https://amass.is.tue.mpg.de/index.html
βοΈ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4
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Autoregressive approach for modeling dynamically deforming human bodies by Meta.
π₯ Github: github.com/facebookresearch/AutoAvatar
βοΈ Project: zqbai-jeremy.github.io/autoavatar
β οΈ Paprer: arxiv.org/pdf/2203.13817.pdf
β© Dataset: https://amass.is.tue.mpg.de/index.html
βοΈ Video: https://zqbai-jeremy.github.io/autoavatar/static/images/video_arxiv.mp4
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π4β€1
π₯ Deep BCI SW ver. 1.0 is released.
π₯ Github: https://github.com/DeepBCI/Deep-BCI
β© Paper: https://arxiv.org/abs/2301.08448v1
β‘οΈ Project: https://deepbci.korea.ac.kr/
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π₯ Github: https://github.com/DeepBCI/Deep-BCI
β© Paper: https://arxiv.org/abs/2301.08448v1
β‘οΈ Project: https://deepbci.korea.ac.kr/
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β
οΈ StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis
π₯ Github: github.com/autonomousvision/stylegan-t
β οΈ Paper: arxiv.org/pdf/2301.09515.pdf
βοΈ Project: sites.google.com/view/stylegan-t
βοΈ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo
π₯ Projected GAN: https://github.com/autonomousvision/projected-gan
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π₯ Github: github.com/autonomousvision/stylegan-t
β οΈ Paper: arxiv.org/pdf/2301.09515.pdf
βοΈ Project: sites.google.com/view/stylegan-t
βοΈ Video: https://www.youtube.com/watch?v=MMj8OTOUIok&embeds_euri=https%3A%2F%2Fsites.google.com%2F&feature=emb_logo
π₯ Projected GAN: https://github.com/autonomousvision/projected-gan
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π₯3π1
β PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
π₯ Github: https://github.com/primeqa/primeqa
π₯ Notebooks: https://github.com/primeqa/primeqa/tree/main/notebooks
β οΈ Paper: https://arxiv.org/abs/2301.09715v2
βοΈ Dataset: https://paperswithcode.com/dataset/wikitablequestions
βοΈ Docs: https://primeqa.github.io/primeqa/installation.html
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π₯ Github: https://github.com/primeqa/primeqa
π₯ Notebooks: https://github.com/primeqa/primeqa/tree/main/notebooks
β οΈ Paper: https://arxiv.org/abs/2301.09715v2
βοΈ Dataset: https://paperswithcode.com/dataset/wikitablequestions
βοΈ Docs: https://primeqa.github.io/primeqa/installation.html
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π1π₯1
π₯ Applied Deep Learning Course
π₯ Github: https://github.com/maziarraissi/Applied-Deep-Learning
β© Paper: https://arxiv.org/pdf/2301.11316.pdf
β‘οΈVideos: https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4
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π₯ Github: https://github.com/maziarraissi/Applied-Deep-Learning
β© Paper: https://arxiv.org/pdf/2301.11316.pdf
β‘οΈVideos: https://www.youtube.com/playlist?list=PLoEMreTa9CNmuxQeIKWaz7AVFd_ZeAcy4
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π7β€1
2301.11696.pdf
871.9 KB
SLCNN: Sentence-Level Convolutional Neural Network for Text Classification
Ali Jarrahi, Leila Safari , Ramin Mousa
abstract: Text classification is a fundamental task in natural language processing (NLP). Several recent studies show the success of deep learning on text processing. Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of text classification. In this paper, new baseline models have been studied for text classification using CNN. In these models, documents are fed to the network as a three-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the text. Besides, analysing adjacent sentences allows extracting additional features. The proposed models have been compared with the state-of-the-art models using several datasets.
Author: @Raminmousa
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Ali Jarrahi, Leila Safari , Ramin Mousa
abstract: Text classification is a fundamental task in natural language processing (NLP). Several recent studies show the success of deep learning on text processing. Convolutional neural network (CNN), as a popular deep learning model, has shown remarkable success in the task of text classification. In this paper, new baseline models have been studied for text classification using CNN. In these models, documents are fed to the network as a three-dimensional tensor representation to provide sentence-level analysis. Applying such a method enables the models to take advantage of the positional information of the sentences in the text. Besides, analysing adjacent sentences allows extracting additional features. The proposed models have been compared with the state-of-the-art models using several datasets.
Author: @Raminmousa
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π6