octave machine learning

http://sourceforge.net/projects/numpy/files/download the corresponding version of the NumPy, everywhere, find a not python2.7Use Pip, please.Pip Install NumPyDownload finished, the hint does not install C + +, meaning is also to install VS2008, but installed is VS2012, had to download a VC for Pythonhttp://www.microsoft.com/en-us/download/confirmation.aspx?id=44266Re-pip, wait for the most of the day, the final count is successfulInput command introduced NumPyFrom numpy Import *Operation:InputRa

1. Common steps 2. Chinese participle 1 This is relative to the English text affective analysis, Chinese unique preprocessing. 2 Common methods: Based on the dictionary, rule-based, Statistical, based on the word annotation, based on artificial intelligence. 3 Common tools: Hit-language cloud, Northeastern University Niutrans statistical Machine translation system, the Chinese Academy of Sciences Zhang Huaping Dr. Ictclas, Posen technology, stutterin

3 Types of Learning3.1 Learning with Different Output Space YThe method of machine learning is categorized from the angle of the output spatial type.1. Two-dollar classification (binary classification): The output label is discrete, two-class.2. Multivariate classification (Multiclass classification): The output label is discrete, multi-class. The dualistic class

Learning plan diagram of actual Java Virtual Machine (Understand Java Virtual Machine), Java Virtual Machine I don't want to talk about it anymore. I am actually using a Java virtual machine. I have to study hard and get started every day! Develop a

first, gradient descent method In the machine learning algorithm, for many supervised learning models, the loss function of the original model needs to be constructed, then the loss function is optimized by the optimization algorithm in order to find the optimal parameter. In the optimization algorithm of machine

1. Vector Norm Norm, Norm, is a concept similar to "Length" in mathematics, which is actually a kind of function.The regularization (regularization) and sparse coding (Sparse coding) in machine learning are very interesting applications.For Vector a∈rn A\in r^n, its LP norm is | | a| | p= (∑IN|AI|P) 1p (1) | | a| | _p= (\sum_i^n |a_i|^p) ^{\frac 1 p} \tag 1Commonly used are: L0 NormThe number of elements i

Cross entropy cost function 1. Cross-entropy theory Cross entropy is relative to entropy, as covariance and variance. Entropy examines the expectation of a single information (distribution): H (p) =−∑I=1NP (xi) Logp (xi) Cross-Entropy examines the expectations of two of information (distributions):H (P,Q) =−∑I=1NP (xi) logq (xi)For details, please see Wiki Cross entropy y = Tf.placeholder (Dtype=tf.float32, Shape=[none, ten]) ... Scores = Tf.matmul (H, W) + b probs = Tf.nn.softmax (scores) l

http://blog.csdn.net/pipisorry/article/details/44119187Machine learning machines Learning-andrew NG Courses Study notesMachine Learning System DesignPrioritizing what do I do on priorityError analysisError Metrics for skewed Classes Error metrics with biased classesTrading Off Precision and recall weigh accuracy and recall rateData for machines

Earlier, we mentioned supervised learning, which corresponds to non-supervised learning in machine learning. The problem with unsupervised learning is that in untagged data, you try to find a hidden structure. Because the examples provided to learners arenot marked, so there

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general functions J (Θ0,θ1,θ2 .....) θn) min J (θ0,θ1,θ2 .....) Θn) How this algorithm works. : Starting from the initial assumption Starting from 0, 0 (or any other valu