🥑 DALL·E Mini
Generate images from a text prompt
Demo: https://huggingface.co/spaces/flax-community/dalle-mini
Github: https://github.com/borisdayma/dalle-mini
Paper: https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA
@Machine_learn
Generate images from a text prompt
Demo: https://huggingface.co/spaces/flax-community/dalle-mini
Github: https://github.com/borisdayma/dalle-mini
Paper: https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA
@Machine_learn
Skansi2018_Book_IntroductionToDeepLearning.pdf
2.5 MB
Introduction
to Deep
Learning
From Logical Calculus to Artificial
Intelligence #book #DL #2018 @Machine_learn
to Deep
Learning
From Logical Calculus to Artificial
Intelligence #book #DL #2018 @Machine_learn
9783446468146.pdf
12.7 MB
Machine Learning
für Zeitreihen
Einstieg in Regressions-, ARIMAund
Deep-Learning-Verfahren
mit Python #Book #DL #2021 @Machine_learn
für Zeitreihen
Einstieg in Regressions-, ARIMAund
Deep-Learning-Verfahren
mit Python #Book #DL #2021 @Machine_learn
Tokyo Olympics Alternative medals table
Article on how teams performed with the respect to behavior expected by regression model.
Link: https://ig.ft.com/tokyo-olympics-alternative-medal-table/
@Machine_learn
Article on how teams performed with the respect to behavior expected by regression model.
Link: https://ig.ft.com/tokyo-olympics-alternative-medal-table/
@Machine_learn
Ft
Tokyo Olympics alternative medals table
Which countries are under-performing and over-performing?
Graph Convolutional Neural Networks to Analyze Complex Carbohydrates
A blog post by Daniel Bojar about an application of GNN to analyzing glycan sequences and their proposed GNN architecture called SweetNet. There are other coverages of this work (here and here). The paper is here and the code is here.
@Machine_learn
A blog post by Daniel Bojar about an application of GNN to analyzing glycan sequences and their proposed GNN architecture called SweetNet. There are other coverages of this work (here and here). The paper is here and the code is here.
@Machine_learn
Github:
https://github.com/OlafenwaMoses/DeepStack_ExDark
Documentation:
https://docs.deepstack.cc/index.html#installation
Custom dataset preparation:
https://docs.deepstack.cc/custom-models/datasetprep/index.html
Training custom model:
https://docs.deepstack.cc/custom-models/training/index.html
@Machine_learn
https://github.com/OlafenwaMoses/DeepStack_ExDark
Documentation:
https://docs.deepstack.cc/index.html#installation
Custom dataset preparation:
https://docs.deepstack.cc/custom-models/datasetprep/index.html
Training custom model:
https://docs.deepstack.cc/custom-models/training/index.html
@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…»
ByT5: Towards a token-free future with pre-trained byte-to-byte models
Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.
Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.
The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.
Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5
#nlp #deeplearning #transformer #pretraining
@Machine_learn
Pre-trained language models usually operate on the sequences of tokens, which are based on words or subword units.
Token-free models operate directly on the raw text (characters or bytes) instead. They can work with any language, are more robust to the noise, and don’t require preprocessing.
The authors use a modified mT5 architecture and show that their approach is competitive with token-level models.
Paper: https://arxiv.org/abs/2105.13626
Code: https://github.com/google-research/byt5
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-byt5
#nlp #deeplearning #transformer #pretraining
@Machine_learn
Reproducible_Bioinformatics_with_Python_by_Ken_Youens_Clark_Ken.pdf
6.2 MB
Reproducible Bioinformatics with Python
How to Write, Document, and Test Programs for
Biology
Ken Youens-Clark
#book #2021 #python #RL
@Machine_learn
How to Write, Document, and Test Programs for
Biology
Ken Youens-Clark
#book #2021 #python #RL
@Machine_learn
Leordeanu_M_Unsupervised_learning_in_space_and_time_Springer_2020.pdf
6.2 MB
Unsupervised Learning in Space and Time
A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
#book #2020 #ML #DL
@Machine_learn
A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks
#book #2020 #ML #DL
@Machine_learn
🔍 Contrastive Sensor Fusion
Github: https://github.com/descarteslabs/contrastive_sensor_fusion
Paper: https://arxiv.org/abs/2108.05094v1
@Machine_learn
Github: https://github.com/descarteslabs/contrastive_sensor_fusion
Paper: https://arxiv.org/abs/2108.05094v1
@Machine_learn
📹 Internal Video Inpainting by Implicit Long-range Propagation
Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting
Paper: https://arxiv.org/abs/2108.01912v1
4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos
Dataset: https://paperswithcode.com/dataset/videoremoval4k
@Machine_learn
Github: https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting
Paper: https://arxiv.org/abs/2108.01912v1
4k Data: https://github.com/Tengfei-Wang/Annotated-4K-Videos
Dataset: https://paperswithcode.com/dataset/videoremoval4k
@Machine_learn