بسم الله الرحمن الرحیم

ماه رمضان [همان ماه ] است که در آن، قرآن فرو فرستاده شده است، [کتابى ] که مردم را راهبر، و[متضمن ] دلایل آشکار هدایت، و[میزان ] تشخیص حق از باطل است. پس هر کس از شما این ماه را درک کند باید آن را روزه بدارد، و کسى که بیمار یا در سفر است [باید به شمارهء آن،] تعدادى از روزهاى دیگر[را روزه بدارد]. خدا براى شما آسانى مى خواهد و براى شما دشوارى نمى خواهد؛ تا شمارهء[مقرر] را تکمیل کنید و خدا را به پاس آنکه رهنمونیتان کرده است به بزرگى بستایید، و باشد که شکرگزارى کنید.
( بقره ۱۸۵)

رمضان مبارک
@Machine_learn

Hyperparameter optimization in python. Part 1: Scikit-Optimize.

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@Machine_learn

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https://towardsdatascience.com/hyperparameter-optimization-in-python-part-1-scikit-optimize-754e485d24fe

Data Science Essentials in Python — Dmitry Zinoviev (en) 2916
#book
#middle
@Machine_learn

Data Science Essentials in Python — Dmitry Zinoviev (en) 2916
#book
#middle
@Machine_learn

#Reinforcement Learning, Fast and Slow
#paper
@Machine_learn

#Reinforcement Learning, Fast and Slow
#paper
@Machine_learn

#ECG_Classification #CNN #Code #Author:@RaminMousa @Machine__learn link:https://github.com/Ramin1Mousa/ECG-/blob/master/Untitled.ipynb

#Top 10 algorithms in data mining
#C4.5, #k-Means, #SVM,
#Apriori, #EM, #PageRank, #AdaBoost, #kNN, #Naive Bayes, and #CART
@Machine_learn

#Top 10 algorithms in data mining
#C4.5, #k_Means, #SVM,
#Apriori, #EM, #PageRank, #AdaBoost, #kNN, #Naive Bayes, and #CART
@Machine_learn

How to Visualize Filters and Feature Maps in Convolutional Neural Networks


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@Machine_learn

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https://machinelearningmastery.com/how-to-visualize-filters-and-feature-maps-in-convolutional-neural-networks/

#Big Data Analysis for
Bioinformatics and
Biomedical Discoveries
#book
#big_data @Machine_learn

#Big Data Analysis for
Bioinformatics and
Biomedical Discoveries
#book
#big_data @Machine_learn

Snake Wrangling for Kids — Jason R. Briggs (en) 2007
#beginner #book
@Machine_learn

Snake Wrangling for Kids — Jason R. Briggs (en) 2007
#beginner #book
@Machine_learn

Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning

Github : https://github.com/tensorflow/graphics

Article: https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668

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@Machine_learn

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#PyTorch Internals
#Note
@Machine_learn

https://web.mit.edu/~ezyang/Public/pytorch-internals.pdf

Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
#Challenge

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@Machine_learn


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https://ai.googleblog.com/2019/05/announcing-open-images-v5-and-iccv-2019.html

#Computer Vision news from RSIP VISION. April 2019
#News
@Machine_learn

#Computer Vision news from RSIP VISION. April 2019
#News
@Machine_learn

Computer Age Statistical Inference - Algorithms, Evidence, & Data Science
Table of Content:
Part I Classic Statistical Inference
1 Algorithms and Inference
2 Frequentist Inference
3 Bayesian Inference
4 Fisherian Inference and Maximum Likelihood Estimation
5 Parametric Models and Exponential Families
Part II Early Computer-Age Methods
6 Empirical Bayes
7 James–Stein Estimation and Ridge Regression
8 Generalized Linear Models and Regression Trees
9 Survival Analysis and the EM Algorithm
10 The Jackknife and the Bootstrap
11 Bootstrap Confidence Intervals
12 Cross-Validation and Cp Estimates of Prediction Error
13 Objective Bayes Inference and MCMC
14 Postwar Statistical Inference and Methodology
Part III Twenty-First-Century Topics
15 Large-Scale Hypothesis Testing and FDRs
16 Sparse Modeling and the Lasso
17 Random Forests and Boosting
18 Neural Networks and Deep Learning
19 Support-Vector Machines and Kernel Methods
20 Inference After Model Selection
21 Empirical Bayes Estimation
#book
@Machine_learn