*Original certificates format:*PDF.

*Converted image format :*PNG.

*Images resolution:*Low-res.

Some my MOOC certificates (statement of accomplishments / honor code certificates). As a matter of memento (personal memorandum).

*Original certificates format: *PDF.

*Converted image format : *PNG.

*Images resolution: *Low-res.

Following previous post "Singular Value Decomposition and Dimensionality, Using R...", here is another approach using Numpy and Scipy.

An example is in Latent Semantic Analysis (LSA, or Latent Semantic Indexing LSI) with Term-Document matrix.

First data is a list of documents, second data is a list of terms. We build a matrix in which each cell represents "is term t in document d?". It is "1" if term t is found in document d, "0" otherwise. In this case, documents are the features (columns) and terms are the observations (rows).

This is usually used in NLP (Natural Language Processing) to calculate text similarity.

An example is in Latent Semantic Analysis (LSA, or Latent Semantic Indexing LSI) with Term-Document matrix.

First data is a list of documents, second data is a list of terms. We build a matrix in which each cell represents "is term t in document d?". It is "1" if term t is found in document d, "0" otherwise. In this case, documents are the features (columns) and terms are the observations (rows).

This is usually used in NLP (Natural Language Processing) to calculate text similarity.

Imagine online store, e.g. Amazon, to have million of items, and million of users. In order to perform algorithm for the recommender system, matrix to be used would have million by million dimension. Which is very expensive computation.

Theory for dimensionality reduction is everywhere, so we won’t repeat it again in here. Just remember the basic equation:

`X = U A V.T`

U matrix has dimension of n x n.V matrix has dimension of d x d.

A matrix is diagonal matrix with dimension of n x d.

We want to reduce the dimension of X matrix.

Power Pivot is delivered with Excel 2013. This is workaround to install it for Excel 2010.

Just playing around in the course, to get in depth a bit on spreadsheet. I'm newbie =)

https://www.edx.org/course/data-analysis-take-it-max-delftx-ex101x

Instructor: Prof. Felienne Hermans, PhD.

https://www.edx.org/course/data-analysis-take-it-max-delftx-ex101x

Instructor: Prof. Felienne Hermans, PhD.

Requirements:

Set environment variable M2_HOME, point it to the Maven directory. E.g. "C:\java\apache-maven-3.3.3" in Windows.

Add path of the LensKit's and Maven's binary directories,

C:\java\lenskit-2.2\bin;%M2_HOME%\bin;%JAVA_HOME%\bin

- Maven
- Intellij IDEA (JetBrains)
- LensKit
- JDK (for sure =P...) Set JAVA_HOME.

Set environment variable M2_HOME, point it to the Maven directory. E.g. "C:\java\apache-maven-3.3.3" in Windows.

Add path of the LensKit's and Maven's binary directories,

C:\java\lenskit-2.2\bin;%M2_HOME%\bin;%JAVA_HOME%\bin

https://www.coursera.org/course/webgl/

**Assignment 1. **

Tesselation and rotation with WebGL. Rotation on tesselated polygon resulted in a twisting effect.

**Disclaimer:** The sharing of codes in public repository is mandatory, ruled by the assignment rubric. It's not violation to the Honour Code.

Tesselation and rotation with WebGL. Rotation on tesselated polygon resulted in a twisting effect.

Finding good lambda ( **Î» **) for regularization in a machine learning model is important, to avoid under-fitting (high bias) or over-fitting (high variance).

If lambda is too large, then all theta ( Î¸ ) values will be penalized heavily. Hypothesis ( h ) tends to zero. (High bias, under-fitting).

If lambda is too small, that's similar to very small regularization. (High variance, over-fitting).

Cross validation set principle can be used to select good lambda based on the plot of errors vs lambda, for both training data and validation data.

If lambda is too large, then all theta ( Î¸ ) values will be penalized heavily. Hypothesis ( h ) tends to zero. (High bias, under-fitting).

If lambda is too small, that's similar to very small regularization. (High variance, over-fitting).

Cross validation set principle can be used to select good lambda based on the plot of errors vs lambda, for both training data and validation data.

Many Machine Learning books I encountered are too heavily math-wise (for a programmer). But I noted several introductory books,

- Machine Learning, Tom M. Mitchell, McGraw Hill.
- Introduction to Machine Learning 2nd edition, Ethem Alpaydin, MIT Press. (without example code)
- Bayesian Reasoning and Machine Learning, David Barber (this has free online draft version, last draft is dated Dec 13, 2014) (ex. code in Matlab with BRMLToolbox).
- Machine Learning, A Probabilistic Perspective, Kevin P Murphy, MIT Press. (ex. code in Matlab with PMTK package.)
- Machine Learning, An Algorithmic Perspective, Stephen Marsland, CRC Press. (ex. code in Python)
- Machine Learning, Hands-On for Developers and Technical Professionals, Jason Bell, Wiley. (ex. code in Java with Weka toolkit.)
- Machine Learning In Action, Peter Harrington, Manning. (ex. code in Python.)
- Thoughtful Machine Learning, a Test Driven Approach, Matthew Kirk, O'Reilly. (ex. code in Ruby.)

More programming-wise books,

- Mastering Machine Learning with scikit-learn, Gavin Hackeling, Packt.
- Learning scikit-learn: Machine Learning in Python, RaÃºl Garreta et.al., Packt.
- scikit-learn Cookbook, Trent Hauck, Packt.
- Building Machine Learning Systems with Python, Willi Richert et.al, Packt.

- An Introduction to Statistical Learning with Applications in R, Gareth James et.al, Springer.
- Machine Learning with R, Brett Lantz, Packt.

- Scala for Machine Learning, Patrick R Nicolas, Packt.

Stanford's Prof. Andrew Ng https://www.coursera.org/course/ml (old regular format with SoA, already closed since 2015).

New format of the course is on-demand (self-paced), currently without SoA, https://www.coursera.org/learn/machine-learning .

----

This is a note on implementation of handwritten digits recognition, with the neural network learning process, by using Octave **nnet **package (or MATLAB neural network toolbox).

At the end, I play around with R code and**RSNNS **library (Stuttgart Neural Network Simulator for R).

GitHub, Octave/MATLAB:

https://github.com/flyingdisc/handwritten-digits-recognition-octave-nnet

Github, R - RSNNS:

https://github.com/flyingdisc/handwritten-digits-recognition-RSNNS

----

At the end, I play around with R code and

GitHub, Octave/MATLAB:

https://github.com/flyingdisc/handwritten-digits-recognition-octave-nnet

Github, R - RSNNS:

https://github.com/flyingdisc/handwritten-digits-recognition-RSNNS

----

Wikipedia's Pareto Principle,

"ThePareto principle(also known as the80–20 rule, thelaw of the vital few,and theprinciple of factor sparsity) states that, for many events, roughly 80% of the effects come from 20% of the causes."

- Pareto Principle, Grow Your Business, Forbes article.
- SixSigma article.
- etc. (google it).

This is an attempt to compare downloaded database with the online version, to find conclusion what is meaning of each value actually.

This analysis is inspired by a post made by David Hood, himself is CTA in the Data Science Specialization courses.

At the end of this article, there is more accurate analysis done by Eddie Song.

It is **Spectral Modeling Synthesis sms-tools** of **MTG UPF** (Music Technology Group, Universitat Pompeu Fabra, Barcelona), by Prof. Xavier Serra (also as instructor in the Audio Signal Processing for Music Applications, Coursera).

http://mtg.upf.edu/technologies/sms

https://github.com/MTG/sms-tools

https://github.com/MTG/essentia

Several simple experiments I made.

http://mtg.upf.edu/technologies/sms

https://github.com/MTG/sms-tools

https://github.com/MTG/essentia

Several simple experiments I made.

Toshiba U20 USB mouse uses only 4 wires. While Logitech uses 5 wires.

In some hardware circumstances (laptop in my case), the Toshiba mouse won't work, it's not detected, keeps blinking and the cursor doesn't move.

This is a simple workaround I tried successfully.

In some hardware circumstances (laptop in my case), the Toshiba mouse won't work, it's not detected, keeps blinking and the cursor doesn't move.

This is a simple workaround I tried successfully.

One of my mobile broadband Internet provider (EVDO Rev A) planned to close its service in a near future, due to Qualcomm has decided to stop EvDO evolution. The service has real unlimited budget-plan. Currently most of mo-bro providers (both CDMA and GSM) in here are moving to early stages of 4G LTE (either FDD or TDD).

So the impact is, many BTS (base transceiver station) nearby my house have been sold, signal was going worse. Several months ago my EvDO modem (without external antenna) could easily resonated at -70 dBm of HDR power. But since the collapse was announced, signal was suffering at only around -85 down to -90 dBm typically.

(*with external antenna, signal can be received at -50 dBm, but I prefer the antenna for another HSPA+ service*)

Fortunately I keep an old unused indoor TV antenna. Sure it's dedicated for lower frequency band for TV broadcast. While that my dying ISP works at higher 800MHz of carrier frequency.

Hoho... then I imagined to build a simple dipole antenna to recycle that my old antenna.

So the impact is, many BTS (base transceiver station) nearby my house have been sold, signal was going worse. Several months ago my EvDO modem (without external antenna) could easily resonated at -70 dBm of HDR power. But since the collapse was announced, signal was suffering at only around -85 down to -90 dBm typically.

(

Fortunately I keep an old unused indoor TV antenna. Sure it's dedicated for lower frequency band for TV broadcast. While that my dying ISP works at higher 800MHz of carrier frequency.

Hoho... then I imagined to build a simple dipole antenna to recycle that my old antenna.

"Bubble Clusters" : Clustering
algorithm, Bubble's Potential Field algorithm, Iso-Surface &
Iso-Line with Marching-Square algorithm.

Assignment #1 of "Interactive Computer Graphics", a course by Prof, Takeo Igarashi, University of Tokyo.

Assignment #1 of "Interactive Computer Graphics", a course by Prof, Takeo Igarashi, University of Tokyo.

Consider a 2D signal (or impulse response of 2D filter) with rect-shaped.

This 2D rect signal is separable. It can be represented as dot-product of two independent 1D signals.

As well an impulse signal is also separable.

These tools have been already known since long time ago.

Pointing is important to get best signal for our broadband/wifi modem, either with or without external antenna.

(Works only for modem with Qualcomm chipset, and the modem's diagnostic port must be opened.)

Howto:

Pointing is important to get best signal for our broadband/wifi modem, either with or without external antenna.

(Works only for modem with Qualcomm chipset, and the modem's diagnostic port must be opened.)

Howto:

Standard JPEG compression uses (1) 8x8 Discrete Cosine Transform, (2) quantization based on certain luminance + chrominance tables, and (3) entropy-encoding (Huffman coding).

Here, I'm using OpenCV (Python) to simulate DCT + quantization + IDCT, without Huffman coding.

Here, I'm using OpenCV (Python) to simulate DCT + quantization + IDCT, without Huffman coding.

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