It's been a long time since I posted it, @ kenmatsu4: blush: It's been a while, but it's not a new post but a summary of the articles so far: sweat_smile: It's been half a year since I said I'd write a summary around the end of last year, but I'll finally release it. I tried to categorize it into statistics, machine learning, programming, math, and more.
| Category | Title |
|---|---|
| General | [Statistics] First "standard deviation" (to avoid frustration with statistics) < / strong> |
| A description of the standard deviation for those who are completely unfamiliar with statistics. We will start with the explanation of the Σ symbol. | |
| General | Basics of Statistics |
| Lecture slides on the basics of statistics. (With Python code that draws the graph) | |
| General | Understanding the meaning of complex and bizarre normal distribution formulas |
| If you look closely at the formula for the normal distribution, you can see that this formula is meaningful. | |
| General | [Statistics] Let's visualize the relationship between the normal distribution and the chi-square distribution. |
| The chi-square distribution is closely related to the normal distribution. | |
| General | [Statistics] Grasp the image of the central limit theorem with a graph |
| I will explain with a graph what the central limit theorem, which is said to be the most important theorem in statistics, is. | |
| General | [Statistics] What is the likelihood? Let's explain graphically. |
| I will explain the likelihood that is difficult to read. It's not a dog degree w | |
| General | [Statistics] Understand what an ROC curve is by animation. |
| I will explain the ROC curve, which is a difficult concept, with animation. | |
| General | [Statistics] Understand the mechanism of Q-Q plot by animation. |
| I will explain the mechanism of Q-Q plot, which is a more difficult concept, using animation. | |
| Regression analysis | Explanation of the concept of regression analysis using Python Part 1 |
| I will explain how the logic for drawing lines in regression analysis works. | |
| Regression analysis | Explanation of the concept of regression analysis using python Part 2 |
| I will explain how the logic for drawing lines in regression analysis works. | |
| Regression analysis | Explanation of the concept of regression analysis using Python Extra 1 |
| I will explain how the logic for drawing lines in regression analysis works. | |
| Regression analysis | [Statistics] Visualization for understanding Generalized Linear Mixed Models (GLMM). |
| Animated explanation of a complex generalized linear mixed model with multiple distributions. | |
| Regression analysis | [Statistics] [R] Try using quantile regression. |
| Introducing regression analysis using quantiles (75% quantiles, etc.) instead of means. | |
| Regression analysis | [Statistics] Try to draw a regression line with the feeling that there may be outliers < / strong> |
| Introducing the R library that handles outliers well. | |
| MCMC | [Statistics] Let's explain sampling by Markov chain Monte Carlo method (MCMC) with animation. |
| We will explain the operating principle of MCMC using animation. | |
| MCMC | [Statistics] Visualize and understand the Hamiltonian Monte Carlo method with animation. |
| This is an article that explains the Hamiltonian Monte Carlo method, which is an improvement of the Metropolis-Hastings method, using animation. | MCMC | [Statistics] Let's explain the execution of logistic regression in stan in detail (w / Titanic dataset)
| This is also a demonstration using the MCMC library Stan.
| MCMC |
[Statistics] Multiprocessing of MCMC sampling
| This is a code explanation for parallelizing MCMC sampling and making effective use of multi-core.
| Principal component analysis
| Principal component analysis Analyze handwritten numbers using PCA. Part 1
| Let's do principal component analysis with a handwritten digit dataset.
| Principal component analysis
| Principal component analysis Analyze handwritten numbers using PCA. Part 2
| Let's try the main component Bunsei with a handwritten digit data set.
| Time series analysis
| Implementation of particle filter by Python and application to state space model
| This is an explanation of the mechanism of the particle filter, which is an online version of MCMC for estimating the state space model.
| Time series analysis
| Call dlm from python to run a time-varying coefficient regression model td>
| This is a state-space model that expresses a model in which the regression coefficient changes over time.
| Time series analysis
| [Statistics] [Time series analysis] Plot the ARMA model to grasp the tendency.
| This is an article to get a feel for plotting the relationship between the parameters of the time series model ARMA and the shape of the graph.
| |
| Category | Title |
|---|---|
| General | Thorough explanation of EM algorithm |
| Using the mixed Gaussian distribution as a theme, we will carefully explain the EM algorithm, which is a well-known algorithm in the machine learning area. | |
| General | Thorough explanation of EM algorithm (bonus) ~ In case of MAP estimation ~ |
| In addition to the above explanation, I will explain a little about how to apply MAP estimation. | |
| General | Explain what stochastic gradient descent is by using Python > |
| Animated explanation of stochastic gradient descent, one of the optimization algorithms. | |
| General | [Machine learning] What is the LP norm? |
| Try to understand by illustrating the LP norm that you do not know at first glance. | |
| General | [Machine learning] Summary and execution of model evaluation / indicators (w / Titanic dataset) |
| It's sober, but it's important to measure the performance of the model properly. It is a commentary for that. | |
| General | [Machine learning] OOB (Out-Of-Bag) and its ratio |
| This is also plain, but it's an interesting story that the number of Napiers $ e $ was hidden behind the bootstrap sampling. | |
| General | Derivation of Kullback Leibler Divergence for multivariate normal distribution |
| Explains the details of the calculation when the multivariate normal distribution is applied to the probability model of Kullback-Leibler divergence. | |
| Handwritten numbers | Playing handwritten numbers with python Part 1 |
| This is an article to play with MNIST, a dataset of handwritten numbers that everyone often uses. | |
| Handwritten numbers | Play handwritten numbers with python Part 2 (identify) |
| Try to identify handwritten characters by the template matching method. | |
| Handwritten numbers | [Machine learning] Write the k-nearest neighbor method (k-nearest neighbor method) in python by yourself. Recognize handwritten numbers |
| A better way to identify handwritten characters using the k-nearest neighbor method. | |
| Deep Learning | [Machine learning] I will explain while trying the deep learning framework Chainer. |
| The better performance, Neural Network, is used to identify handwritten characters. | |
| Deep Learning | [Deep learning] Try Autoencoder with Chainer and visualize the result. |
| Try to figure out who the AutoEncoder used in Deep Learning is. | |
| Deep Learning | Variational Autoencoder Thorough Explanation |
| I explained in detail the Variational Autoencoder (VAE), which is the basic model of the generative model generated by Deep Learning. | |
| Spark MLlib | [Machine learning] Start Spark with iPython Notebook and try MLlib < / td> |
| Environment settings for using Spark's machine learning library MLlib. | |
| Spark MLlib | [Machine learning] Try running Spark MLlib with Python and make recommendations |
| Let's make a recommendation using MLlib. | |
| Spark MLlib | [Machine learning] Cluster Yahoo News articles with MLlib topic model (LDA). |
| We will also try the topic model of the topic with MLlib. | |
| Anomaly detection | [Machine learning] "Anomaly detection and change detection Chapter 1" Fill in the space between the lines of the Neyman-Pearson lemma See |
| I tried to fill the space between the lines of the formula in the anomaly detection book. | |
| Anomaly detection | [Machine learning] "Anomaly detection and change detection" Let's draw the figure of Chapter 1 in Python. |
| I drew a graph to improve the understanding of the anomaly detection book. | |
| Anomaly detection | Use R density ratio estimation package densratio from Python |
| Calling R package from Python I tried to detect anomaly using rpy2 and R density ratio estimation package dens ratio. | |
| sparse | [PyStan] Try Graphical Lasso with Stan |
| Graphical Lasso is an article to confirm with Stan that it is a multivariate normal distribution in which the Laplace distribution is set as the prior distribution of the precision matrix. | |
| Summary | Machine Learning Professional Series Round Reading Session Slide Summary |
| A collection of slides used in machine learning study sessions. I recommend it because there are many good materials! | |
| Category | Title |
|---|---|
| Get a large amount of Starbucks Twitter data with python and try data analysis Part 1 strong> | |
| Try using Python to call Twitter's REST API to save the data. | |
| Get a large amount of Starbucks Twitter data with python and try data analysis Part 2 strong> | |
| Separate spam from the retrieved Twitter data. | |
| Get a large amount of Starbucks Twitter data with python and try to analyze the data Part 3 strong> | |
| Let's analyze the reason why the number of tweets increased after one day. | |
| Visualization and analysis of Starbucks Twitter data location information | |
| Visualize the location information hidden in Twitter. | |
| Attempt a rudimentary sentiment analysis on Twitter Stream API data. | |
| Try to analyze emotions using the Japanese evaluation polarity dictionary. | |
| Get information on the 100 most influential tech Twitter users in the world with python. | |
| This is a scraping study. | |
| Convenience book | Private Python handbook (updated from time to time) |
| I have a handy book of Python tricks that I often use. | |
| Graphics | Preferences for generating animated GIFs from Python on Mac |
| This is a method I often use, a setting method for generating animations. | |
| Graphics | Video conversion process by moviepy with ndarray |
| I will explain the procedure for processing videos. It is used when the video is subjected to deep learning and the result is further output as a video. | |
| Graphics | [Python] Customize Colormap when drawing graphs with matplotlib |
| This is a trick when you want to adjust the color of the graph exquisitely. | |
| Cython | Using Cython with Jupyter Notebook [Python] |
| This is an explanation of how to try Cython speedup on Jupyter Notebook. | |
| Word Cloud | Visualize the word appearance frequency of sentences with Word Cloud. [Python] |
| This is an explanation of how to make it possible to understand words that are often displayed in a certain sentence at a glance. | |
| Graph DB | Introduction to Graph Database Neo4j in Python for Beginners (for Mac OS X) |
| I tried a new type of database graph database. | |
| Julia | Try running Julia with Jupyter for regression analysis. |
| I explained from installation to execution of regression analysis in the popular programming language Julia. | |
| Category | Title |
|---|---|
| Mathematics | [Mathematics] If you understand the meaning of "inner product" graphically, you can see various things 1 < / strong> |
| The inner product can be calculated, but some people may not immediately think of a pictorial image. This article is a commentary for understanding the dot product graphically. | |
| Mathematics | [Mathematics] Let's visualize what eigenvalues and eigenvectors are. / td> |
| I think some of you may find it difficult to get a pictorial image of the eigenvalues and eigenvectors. This is also an article that I tried to explain using a lot of animation. | |
| Mathematics | Intuitive understanding of Jensen's inequality |
| This is an article that shows how Jensen's inequality regarding random variables can be understood graphically. | |
| Mathematics | The meaning of fractional division understood in pizza |
| It's a little bit of a story ..., but I really like it. | |
| Summary | [Qiita API] [Statistics / Machine Learning] I tried to summarize and analyze the articles posted so far. |
| It's a little old, but I analyzed the data of the article I wrote. | |
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