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Amazon, Berkeley release dataset of product images and metadata.
Dataset includes multiple images of 147,702 products, including 360° rotations and 3-D models for thousands of them.
https://www.amazon.science/blog/amazon-berkeley-release-dataset-of-product-images-and-metadata
@Machine_learn
Dataset includes multiple images of 147,702 products, including 360° rotations and 3-D models for thousands of them.
https://www.amazon.science/blog/amazon-berkeley-release-dataset-of-product-images-and-metadata
@Machine_learn
Breast Cancer Wisconsin (Diagnostic) Data Set
Predict whether the cancer is benign or malignant
Here is link of dataset: Link
🔷 Number of instances: 569
🔷 Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
🔷 Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)
#dataset
@Machine_learn
Predict whether the cancer is benign or malignant
Here is link of dataset: Link
🔷 Number of instances: 569
🔷 Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
🔷 Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)
#dataset
@Machine_learn
Self-Supervised Learning with Swin Transformers
Github: https://github.com/SwinTransformer/Transformer-SSL
Paper: https://arxiv.org/abs/2105.04553v2
@Machine_learn
Github: https://github.com/SwinTransformer/Transformer-SSL
Paper: https://arxiv.org/abs/2105.04553v2
@Machine_learn
SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping
Paper:
https://arxiv.org/pdf/2105.07014.pdf
Video:
https://www.youtube.com/watch?v=W7NCbfZp6QE
Code:
https://github.com/google-research/google-research/tree/master/smurf
@Machine_learn
Paper:
https://arxiv.org/pdf/2105.07014.pdf
Video:
https://www.youtube.com/watch?v=W7NCbfZp6QE
Code:
https://github.com/google-research/google-research/tree/master/smurf
@Machine_learn
GIRAFFE: A Closer Look at the Code for CVPR 2021’s Best Paper
[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf
[Source] https://github.com/autonomousvision/giraffe
[Blog] https://autonomousvision.github.io/giraffe/
[Interactive slides] https://m-niemeyer.github.io/slides/#/4
[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html
@Machine_learn
[Paper] http://www.cvlibs.net/publications/Niemeyer2021CVPR.pdf
[Source] https://github.com/autonomousvision/giraffe
[Blog] https://autonomousvision.github.io/giraffe/
[Interactive slides] https://m-niemeyer.github.io/slides/#/4
[Collected] https://m-niemeyer.github.io/project-pages/giraffe/index.html
@Machine_learn
Master_Machine_Learning_Algorithms_Discover_how_they_work_by_Jason.pdf
1.1 MB
Jason Brownlee
Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch
#Ml #book
@Machine_learn
Master Machine Learning Algorithms Discover How They Work and Implement Them From Scratch
#Ml #book
@Machine_learn
EL-Attention: Memory Efficient Lossless Attention for Generation
Github: https://github.com/microsoft/fastseq
Paper: https://arxiv.org/abs/2105.04779v1
@Machine_learn
Github: https://github.com/microsoft/fastseq
Paper: https://arxiv.org/abs/2105.04779v1
@Machine_learn
GitHub
GitHub - microsoft/fastseq: An efficient implementation of the popular sequence models for text generation, summarization, and…
An efficient implementation of the popular sequence models for text generation, summarization, and translation tasks. https://arxiv.org/pdf/2106.04718.pdf - microsoft/fastseq
Deep Learning Dataset For Passage and Document Retrieval
Github: https://github.com/grill-lab/DL-Hard
Paper: https://arxiv.org/abs/2105.07975v1
@Machine_learn
Github: https://github.com/grill-lab/DL-Hard
Paper: https://arxiv.org/abs/2105.07975v1
@Machine_learn
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Cartoon-StyleGan2 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation
Github: https://github.com/happy-jihye/Cartoon-StyleGan2
Paper: https://arxiv.org/abs/2106.12445
Colab: https://colab.research.google.com/github/happy-jihye/Cartoon-StyleGan2/blob/main/Cartoon_StyleGAN2.ipynb
@Machine_learn
Github: https://github.com/happy-jihye/Cartoon-StyleGan2
Paper: https://arxiv.org/abs/2106.12445
Colab: https://colab.research.google.com/github/happy-jihye/Cartoon-StyleGan2/blob/main/Cartoon_StyleGAN2.ipynb
@Machine_learn
🌠 Deepmind's Generally capable agents emerge from open-ended play
Blog : https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play
Paper: https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents
DeepMind Research: https://github.com/deepmind/deepmind-research
Video: https://www.youtube.com/watch?v=lTmL7jwFfdw&ab_channel=DeepMind
@Machine_learn
Blog : https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play
Paper: https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents
DeepMind Research: https://github.com/deepmind/deepmind-research
Video: https://www.youtube.com/watch?v=lTmL7jwFfdw&ab_channel=DeepMind
@Machine_learn
A Dataset for Studying Gender Bias in Translation
http://ai.googleblog.com/2021/06/a-dataset-for-studying-gender-bias-in.html
@Machine_learn
http://ai.googleblog.com/2021/06/a-dataset-for-studying-gender-bias-in.html
@Machine_learn
research.google
A Dataset for Studying Gender Bias in Translation
Posted by Romina Stella, Product Manager, Google Translate Advances on neural machine translation (NMT) have enabled more natural and fluid transla...
10.5445IR1000131732.pdf
74.8 MB
Deep Learning based Vehicle Detection in Aerial Imagery
Sommer, Lars Wilko #2021 #book #DL @Mchine_learn
Sommer, Lars Wilko #2021 #book #DL @Mchine_learn
Ketkar-Moolayil2021_Book_DeepLearningWithPython.pdf
5.2 MB
Deep Learning
with Python
Learn Best Practices of
Deep Learning Models
with PyTorch Ketkar, Nikhil ; Moolayil, Jojo #2021 #DL #Book #PyTorch @Machine_learn
with Python
Learn Best Practices of
Deep Learning Models
with PyTorch Ketkar, Nikhil ; Moolayil, Jojo #2021 #DL #Book #PyTorch @Machine_learn
با عرض سلام ما پكيج ٣٦ پروژه عملي با يادگيري عميق همراه با داكيومنت فارسي را براي دوستاني كه مي خواهند در اين حوزه به صورت عملي كار كنند تهيه كرديم سرفصل هاي اين پكيج به ترتيب زير مي باشند:
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
هزينه اين پكيج ٥٠٠هزار مي باشد و صرفا هزينه تهيه ديتاست هاست.
جهت خريد مي توانيد با ايدي بنده در ارتباط باشيد
@Raminmousa
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
--02_Creating_and_Using_Tensors
--03_Implementing_Activation_Functions
-02_TensorFlow_Way
--01_Operations_as_a_Computational_Graph
--02_Implementing_Loss_Functions
--03_Implementing_Back_Propagation
--04_Working_with_Batch_and_Stochastic_Training
--05_Evaluating_Models
-03_Linear_Regression
--linear regression
--Logistic Regression
-04_Neural_Networks
--01_Introduction
--02_Single_Hidden_Layer_Network
--03_Using_Multiple_Layers
-05_Convolutional_Neural_Networks
--Convolution Neural Networks
--Convolutional Neural Networks Tensorflow
--TFRecord For Deep learning Models
-06_Recurrent_Neural_Networks
--Recurrent Neural Networks (RNN)
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
هزينه اين پكيج ٥٠٠هزار مي باشد و صرفا هزينه تهيه ديتاست هاست.
جهت خريد مي توانيد با ايدي بنده در ارتباط باشيد
@Raminmousa
Machine learning books and papers pinned «با عرض سلام ما پكيج ٣٦ پروژه عملي با يادگيري عميق همراه با داكيومنت فارسي را براي دوستاني كه مي خواهند در اين حوزه به صورت عملي كار كنند تهيه كرديم سرفصل هاي اين پكيج به ترتيب زير مي باشند: 1-Deep Learning Basic -01_Introduction --01_How_TensorFlow_Works…»