From 063b5869d213560a6556479f044533213a3d9854 Mon Sep 17 00:00:00 2001 From: Chakib Benziane Date: Wed, 15 Mar 2017 22:38:06 +0100 Subject: [PATCH] Update README.md --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9b4269d..9717637 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,7 @@ Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning - [Stories and experiences](#stories-and-experiences) - [Machine Learning Algorithms](#machine-learning-algorithms) - [Deep Learning Resources](#deep-learning-resources) +- [Deep Learning To read](#deep-learning-to-read) - [Beginner Books](#beginner-books) - [Practical Books](#practical-books) - [Kaggle knowledge competitions](#kaggle-knowledge-competitions) @@ -103,8 +104,8 @@ This short section were prerequisites/interesting info I wanted to learn before - [x] [Understanding Bayes: A Look at the Likelihood](https://alexanderetz.com/2015/04/15/understanding-bayes-a-look-at-the-likelihood/) - [x] [Logistic Regression and Gradient Descent](http://www.cs.rpi.edu/~magdon/courses/LFD-Slides/SlidesLect09.pdf) - [x] [The Method of Maximum Likelihood for Simple Linear Regression](www.stat.cmu.edu/~cshalizi/mreg/15/lectures/06/lecture-06.pdf) -- [-] [Regression Estimation - Least Squares and Maximum Likelihood](http://www.robots.ox.ac.uk/~fwood/teaching/W4315_Fall2011/Lectures/lecture_3/lecture_3.pdf) -- [-] [SVMs and Kernel Method](http://www.win-vector.com/blog/2011/10/kernel-methods-and-support-vector-machines-de-mystified/) +- [x] [Regression Estimation - Least Squares and Maximum Likelihood](http://www.robots.ox.ac.uk/~fwood/teaching/W4315_Fall2011/Lectures/lecture_3/lecture_3.pdf) +- [x] [SVMs and Kernel Method](http://www.win-vector.com/blog/2011/10/kernel-methods-and-support-vector-machines-de-mystified/) ## Stories and experiences - [ ] [Machine Learning in a Week](https://medium.com/learning-new-stuff/machine-learning-in-a-week-a0da25d59850#.tk6ft2kcg) @@ -139,6 +140,9 @@ This short section were prerequisites/interesting info I wanted to learn before - [ ] [Deep Learning Tensorflow Algorithms](http://deep-learning-tensorflow.readthedocs.io/en/latest/#) - [ ] [Visualizing and Understanding Convolutional Networks](www.matthewzeiler.com/pubs/arxive2013/eccv2014.pdf) +## Deep Learning To Read +- [ ] [Google NN for machine translation](research.googleblog.com/2016/09/a-neural-network-for-machine.html) + ## Beginner Books - [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X) - [ ] [Data Science for Business: What you need to know about data mining and data­ analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)