A lot of online courses for coding skills are available nowadays, both free and non-free. I will collect this kind of websites (ever spent some hours on each one) here. 1. Coursera: Free https://www.coursera.org/ A nice MOOC website for various cours…

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644   How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning   ICLR 2017  2017.03.12  Code and video examples can be found at: https://coxlab.github.io/prednet/ 摘要:基于监督训练的深度学习技术取得了非常大的成功,但是无监督问题仍然是一个未能解决的一大难题(从未标注的数据中学习到…

目录 概 主要内容 从具有序的数据讲起 Contrastive Predictive Coding (CPC) 图片构建序 Den Oord A V, Li Y, Vinyals O, et al. Representation Learning with Contrastive Predictive Coding.[J]. arXiv: Learning, 2018. Henaff O J, Srinivas A, De Fauw J, et al. Data-Efficient Image…

1.Feature Normalization: 归一化的处理 function [X_norm, mu, sigma] = featureNormalize(X) %FEATURENORMALIZE Normalizes the features in X % FEATURENORMALIZE(X) returns a normalized version of X where % the mean value of each feature is 0 and the standard dev…

1.warmUpExercise: function A = warmUpExercise() %WARMUPEXERCISE Example function in octave % A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix A = []; % ============= YOUR CODE HERE ============== % Instructions: Return…

print "Performing greedy feature selection..." score_hist = [] N = 10 good_features = set([]) # Greedy feature selection loop while len(score_hist) < 2 or score_hist[-1][0] > score_hist[-2][0]: scores = [] for f in range(len(Xts)): if f no…

Today when taking a bath I got a good idea that it is an efficient and interesting way to learn a new programming language: (These days I learn Python from the Python manual and feel a little bored....) Learn programming by trying some little or larg…

第一部分 字典学习以及稀疏表示的概要 字典学习(Dictionary Learning)和稀疏表示(Sparse Representation)在学术界的正式称谓应该是稀疏字典学习(Sparse Dictionary Learning).该算法理论包含两个阶段:字典构建阶段(Dictionary Generate)和利用字典(稀疏的)表示样本阶段(Sparse coding with a precomputed dictionary).这两个阶段(如下图)的每个阶段都有许多不同算法可供选择,每种…

来源:http://blog.csdn.net/abcjennifer/article/details/7804962 无监督学习近年来很热,先后应用于computer vision, audio classification和 NLP等问题,通过机器进行无监督学习feature得到的结果,其accuracy大多明显优于其他方法进行training.本文将主要针对Andrew的unsupervised learning,结合他的视频:unsupervised feature learning b…