Looking Back on the Year 2018


   There are only a few days left in 2018, and I think it is time to look back and reflect on what we have done during the year. DataTechNotes has offered 50 articles in a field of data science in this year. Readers visit this blog from different parts of the world every day. As an author of DataTechNotes, I tried to publish a new article every week. I think I've accomplished this goal and this is what I can proud of it so far. Consistency is a key factor in doing projects, small daily steps towards the goal lead big achievements.
   Some of my lessons I would like to highlight here:
  • Consistency is a critical factor whatever you do.
  • Focus and goal give a motivation.
  • The teacher gets the best lessons to himself by teaching the others.
  • Writing is an arrangement of scattered ideas in your mind, and it improves learning ability.
  • Helping people gives you joy and fulfillment.
    To write a new article, I spent a few hours, sometimes I organized the ideas in my mind for weeks. We tackled various topics of data science from basics concepts to advanced topics such as classification, regression, anomaly detection, boosting, deep learning, neural networks, regularization, recurrent neural networks, and others during the year. Example source codes are presented in R, Python, and C# programming languages.
   Here, you may check and read all the posts during the year.

December 2018
RNN Example with Keras SimpleRNN in Python
Time series data prediction with LSTM model in python
Cross-validation in R
Classification with Gaussian Naive Bayes model in Python
Forecasting time series data in Python

November 2018
Classification with Random Forests in Python
Classification with CART model in R
Understanding Max-Pooling of Image Data with R
Dynamic Time Warping Example in R

October 2018
Classification with Learning Vector Quantization in R
LDA Classification in R
Understanding Elastic Net Regularization with R
Understanding Ridge regularization with R
Understanding Lasso regularization with R

September 2018
How to Run R Script from C# Program in a Session
Regression Example with ML.NET in C#
Classification Example with ML.NET in C#
Image Convolution Example in R
Classification with Logistic Regression in Python

August 2018
Understanding Bias and Variance in Model Fitting
Deep Learning with Keras in Python
Deep Learning with Keras in R
K-means clustering with sklearn in Python

July 2018
Classification with MXNet in R
Gradient Descent with Linear Regression model in R
Classification with 'maboost' in R
LogitBoost classification sample in R

June 2018
Polynomial Regression Fitting in Python
Hololens object rotation with gesture
Linear Regression Model Example in Python

May 2018
How to plot in Python.
How To Install Shiny Server on CentOS 7
Solving R crash while loading keras dataset in centos 7
Classification with 'bagging' function in R

April 2018
Classification with a bagging (treebag) method in R
Forecasting time series data in R
Running R script from C# program

March 2018
Introduction of Boosting Algorithms Usage in R
Classification with XGBoost Model in R
Classification with Adaboost Model in R
Classification with Gradient Boosting Model in R

February 2018
Correlation analysis and plotting in R
Polynomial regression curve fitting in R
T-test in R
Bayesian Network in R
Z-score calculation with R

January 2018
Understanding standard deviation and 68-95-99.7 rule with R
Outlier check with kmeans distance calculation in R
Outlier check with SVM novelty detection in R
Understanding data variable types in statistics


   Thank you very much for visiting this blog and reading my articles. I hope, you will find them helpful.
  Best wishes!
  Otabek Yorkinov

2 comments:
  1. Thank you, Yorkinov, for sharing the knowledge. Why not share your life experience also, as other menu of technology. Wondering what is 'behind the scenes'. :)

    ReplyDelete