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

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.

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@Machine_learn


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Youtube Video Lectures: 👇

https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS

Course Lecture Notes: 👇

http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

Rules of Machine Learning:
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn

Rules of Machine Learning:
Best Practices for ML Engineering by Martin Zinkevich best practices in ML from around Google
@Machine_learn

#Food recommender system based on LSTM network and cosine similarity
#Author:@Raminmousa
@Machine_learn
https://github.com/Ramin1Mousa/food-recommendation-system

Adventures in WhatsApp DB — extracting messages from backups (with code examples)
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@Machine_learn


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https://medium.com/@1522933668924/extracting-whatsapp-messages-from-backups-with-code-examples-49186de94ab4

#NLP 2018 Highlights
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn

#NLP 2018 Highlights
By Elvis Saravia.
Summary of all the biggest NLP stories, state-of-the-art results and new interesting research directions of the year coming from both academia and the industry
@Machine_learn

Key Papers in Deep Reinforcement Learning
#deep_learning
#Reinforcement_learning
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@Machine_learn



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https://spinningup.openai.com/en/latest/spinningup/keypapers.html

Estimators, Loss Functions, Optimizers —Core of ML Algorithms
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@Machine_learn

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https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]

#Designing Machine Learning Systems with Python by David Julian
#Book @Machine_learn

#Designing Machine Learning Systems with Python by David Julian
#Book @Machine_learn

TensorWatch: a debugging and visualization tool designed for deep learning
#TensorWatch
#tool #deep_learning
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@Machine_learn

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https://github.com/microsoft/tensorwatch

@Machine_learn #Article_code

Generating Game of Thrones Characters Using StyleGAN
article: https://blog.nanonets.com/stylegan-got/

gitHub repo: https://github.com/iyaja/stylegan-encoder

KB – Neural Data Mining with Python sources — Roberto Bello (en) 2013
#middle #book
@Machine_learn

KB – Neural Data Mining with Python sources — Roberto Bello (en) 2013
#middle #book
@Machine_learn

A Manager’s Guide to Data Warehousing — Laura L. Reeves (en) 2009
#book #beginner @Machine_learn

A Manager’s Guide to Data Warehousing — Laura L. Reeves (en) 2009
#book #beginner @Machine_learn

How to Develop a Deep CNN to Classify Satellite Photos of the Amazon Rainforest
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@Machine_learn


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https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/

@Machine_learn
MNIST reborn, restored and expanded.
Now with an extra 50,000 training samples.

If you used the original #MNIST test set more than a few times, chances are your models #overfit the test set. Time to test them on those extra samples.

Now you will use #QMNIST instead of #MNIST
Detailed explanation at #paper: 👇

https://arxiv.org/pdf/1905.10498.pdf

and it's #implementation and some results by using #pytorch: 👇

https://github.com/facebookresearch/qmnist

#50 AI Influencers to follow on #twitter
@Machine_learn