📶 ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation

Github: https://github.com/segmentationblwx/sssegmentation

Paper: https://arxiv.org/abs/2108.12382v1

Dataset: https://cs.stanford.edu/~roozbeh/pascal-context/

@Machine_learn

🌐 A Partition Filter Network for Joint Entity and Relation Extraction

Github: https://github.com/Coopercoppers/PFN

Paper: https://arxiv.org/abs/2108.12202v2

@Machine_learn

ℹ️ LIGAR: Lightweight General-purpose Action Recognition

Github: https://github.com/openvinotoolkit/training_extensions

Paper: https://arxiv.org/abs/2108.13153v1

Models: https://github.com/openvinotoolkit/training_extensions/tree/develop/misc

@Machine_learn

Lane Detection With OpenCV (Part 1)

1. Intro
2. Thresholding
3. Perspective Correction
4. Warping

https://dzone.com/articles/lane-detection-with-opencv


@Machine_learn

​​Type4Py: Deep Similarity Learning-Based Type Inference for #python

Over the past decade, machine learning (ML) has been applied successfully to a variety of tasks such as computer vision and natural language processing. Motivated by this, in recent years, researchers have employed ML techniques to solve code-related problems, including but not limited to, code completion, code generation, program repair, and type inference.

Dynamic programming languages like Python and TypeScript allows developers to optionally define type annotations and benefit from the advantages of static typing such as better code completion, early bug detection, and etc. However, retrofitting types is a cumbersome and error-prone process. To address this, we propose Type4Py, an ML-based type auto-completion for Python. It assists developers to gradually add type annotations to their codebases.

@Machine_learn

https://github.com/saltudelft/type4py
Announcing post: https://mirblog.net/index.php/2021/07/31/development-and-release-of-type4py-machine-learning-based-type-auto-completion-for-python/

Machine learning using Python
#book #python #ML
@Machine_learn

Learning Neural Causal Models with Active Interventions

Github: https://github.com/nke001/causal_learning_unknown_interventions

Paper: https://arxiv.org/abs/2109.02429v1

@Machine_learn

سلام
دوستاني كه راجع به پياده سازي پايان نامه , مقاله و يا ... مشكل دارند، مي تونن با ايدي بنده در ارتباط باشند.

telg: @Raminmousa

همچنين جهت صحبت كردن راجع به موارد گفته شده مي تونن با Whats app بنده در ارتباط باشند.

Whats app: +989333900804

Paper:
https://arxiv.org/pdf/2105.06993.pdf

Project Page:
https://omnimatte.github.io/

Github:
https://github.com/erikalu/omnimatte


Supplimentary material:
https://omnimatte.github.io/supplementary/index.html

Explained:
https://www.youtube.com/watch?v=lCBSGOwV-_o

@Machine_learn

Reinforcement Learning Lecture Series (2021) - A comprehensive look at modern reinforcement learning by DeepMind & UCL.

Course Page:
https://deepmind.com/learning-resources/reinforcement-learning-series-2021

Lectures:
https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm

@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

Texformer: 3D Human Texture Estimation from a Single Image with Transformers

Github: https://github.com/xuxy09/texformer

Paper: http://arxiv.org/abs/2109.02563

Meta data: https://www.dropbox.com/s/ekxn300cuw8bw6b/meta.zip

@Machine_learn

A study on Ensemble Learning for Time Series
Forecasting and the need for Meta-Learning #Paper #2021 @Machine_learn

Ensemble deep learning: A review #Paper #2021 @Machine_learn

A Survey on Ensemble Learning under the Era of Deep Learning #Paper #2021 @Machine_learn

✅ AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

Github: https://github.com/alibaba/AliceMind

Paper: https://arxiv.org/abs/2109.05687v1

Dataset: https://paperswithcode.com/dataset/glue

@Machine_learn

A New Deep Learning-Based Methodology for Video Deepfake
Detection Using XGBoost #Deepfake #Paper @Machine_learn

ConvXGB: A new deep learning model for classification problems
based on CNN and XGBoost #XGBoost #Paper @Machine_learn

Conformal Prediction Interval for Dynamic Time-Series #Timeseries #Paper @Machine_learn