When dealing with large amount of data, speed is essential. The following link provides some good tips. However, the 23 tips are not very concretely related to my daily workflow, so I summarized the below: Query the large table all at once and once, when querying it, restrict in where clause to make it terse … Continue reading Speed Up SQL Queries

# Machine Learning by Andrew Ng_9 Applications

Having learned a great deal of ML theory, the end goal is for application. Prof.Ng talked about anomaly detection before he went on classical ML application - recommender system and photo OCR, he also touched in between the technique to deal with large scale of data - map reduce, data parallelogram, stochastic gradient descent and … Continue reading Machine Learning by Andrew Ng_9 Applications

# Machine Learning by Andrew Ng_8 K-Means and PCA

Previous discussion is all about supervised learning, meaning there is a labeled or known output data pairs (x, y) for the algorithm to be trained. However, in real world, there are circumstances when intellectual creatures like human beings who can tell patterns without known data or first time. This is realized by another kind of … Continue reading Machine Learning by Andrew Ng_8 K-Means and PCA

# Machine Learning by Andrew Ng_7 Support Vector Machine(SVM)

Another type of ML worth learning is SVM - support vector machine. The math behind is similar or derived from logistic regression with some mathematical substitution to get below: To be more concrete, it's main purpose is to achieve higher margin in terms of vector distance between data points and decision boundary Next, Kernel is … Continue reading Machine Learning by Andrew Ng_7 Support Vector Machine(SVM)

# Machine Learning by Andrew Ng_6 Evaluate ML Hypothesis

After a good grasp of theory of machine learning, we should delve into the application of it. Given my limited experience in using it, I won't go deeper but touch on general topic - evaluate ML hypothesis. It will take a bit significant amount of time before actual ML runnig, but definitely worthy to rigorously … Continue reading Machine Learning by Andrew Ng_6 Evaluate ML Hypothesis

# Machine Learning by Andrew Ng_5 Neuron Network

The hot word "neuron network" is part of machine learning. It follows the same math logic described before except in a more convoluted manners. In essence, the neuron unit can be regarded as one regression unit: Convolution occurs in the sense that there is hidden layer composed of multiple neurons to take into features from … Continue reading Machine Learning by Andrew Ng_5 Neuron Network

# Machine Learning by Andrew Ng_4 Logistic Regression

The previous discussion is meant to solve predicting problem that is on continuous values. What if the value you try to predict is discretionary such as category A, B, or even just binary, positive or negative, yes or no etc. These are classification problems and the tool is not linear, or multivariate or polynomial regression … Continue reading Machine Learning by Andrew Ng_4 Logistic Regression

# Machine Learning by Andrew Ng_3 Normal Equation

A prelude to get deeper into ML, I first like to point out why linear algebra is so important. It's a magic way to collapse tons of sample data and parameter data into a very short, flat, succinct one-line equation: prediction = data matrix * parameters, below uses single factor (size) for housing price prediction … Continue reading Machine Learning by Andrew Ng_3 Normal Equation

# Machine Learning by Andrew Ng_2 Cost Function

Systematic learning of professor Ng's machine learning course at youtube. First, repaste the major topics he covered in this 112-video series: Through out the whole course of machine learning, we need to grasp the essences of it. Cost of function is one the essences. to understand this concept from linear regression is a easy way. … Continue reading Machine Learning by Andrew Ng_2 Cost Function

# Multivariable Calculus_4 Integral of Vector Field and Multiple Integrals

It all starts from the leap jump accomplished by calculus in the attempt to calculate irregular shape. It does't take super smartness to go about by dividing the shape into infinitesimal small pieces, then sum up. But the sum-up is at most an approximation, not a finite, accurate value, what makes it accurate? That leap … Continue reading Multivariable Calculus_4 Integral of Vector Field and Multiple Integrals