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

TI-Capsule: Capsule Network for Stock Exchange Prediction #Paper #2021 Author:@Raminmousa @Machine_learn

Dynamic Slimmable Network (DS-Net)

Github: https://github.com/M3DV/AlignShift

Paper: https://arxiv.org/abs/2109.10060v1 @Machine_learn

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research

CompilerGym is a library of easy to use and performant reinforcement learning environments for compiler tasks.

Github: https://github.com/facebookresearch/CompilerGym

Documents: https://facebookresearch.github.io/CompilerGym/

Paper: https://arxiv.org/abs/2109.08267v1 @Machine_learn

Hands-On Gradient Boosting with XGBoost and scikit-learn
#book #python #XGBoost
@Machine_learn