**I have no special talent. I am only passionately curious.**

**- Albert Einstein**

The year 2019 is coming to an end and now it is time to look back and evaluate our work during the year. First of all, I am grateful for everything this year. I am especially thankful that I've accomplished my publication target for the year and I am honored to write my year-end post to my readers.

In 2019, I published 52 articles on datatechnotes.com. I could keep the persistence in my publications. I believe that the quality of the tutorials has improved compared to the last year.

This year, we have been discussed various topics on machine learning, deep learning, and data analysis. Particularly, we've tackled the regression analysis, accuracy, NLP, clustering, and deep learning topics. Examples in Python have been increased, about seventy percent of the tutorials were written in Python.

I'll keep writing the tutorials on topics of machine learning and deep learning next year too. Topics may include NLP, GANs, LSTM networks, and other interesting concepts of data science.

Below I listed all the posts of 2019. You can check and learn more about them.

Thank you for visiting this blog!

I wish you good luck with your studies and all the bests!

*Otabek Yorkinov*

**Articles of 2019**

*(from December to January)*

- Multi-output Regression Example with Keras LSTM Network in Python
- How to Fit Regression Data with CNN Model in Python
- Multi-output Regression Example with Keras Sequential Model
- Understanding Optimizers in Neural Networks with Keras
- Bayesian Ridge Regression Example in Python
- Least Angle Regression Example in Python
- Understanding Activation Function with Python
- How to Create ROC Curve in Python
- Understanding Batch Normalization with Keras in Python
- Understanding Moving Average with R
- Agglomerative Clustering Example in Python
- Regression Accuracy Check in Python (MAE, MSE, RMSE, R-Squared)
- Clustering Example with Mean Shift method in Python
- Clustering Example with BIRCH method in Python
- How to Use GridSearchCV in Python
- Understanding Dropout Regularization in Neural Networks with Keras in Python
- Support Vector Regression Example with SVM in R
- Regression with Generalized Additive Model (GAM) in R
- ElasticNet Regression Example in Python
- Understanding Blockchain Programming with R
- Lasso Regression Example in Python
- Ridge Regression Example in Python
- Regression Example with AdaBoostRegressor in Python
- Classification Example with XGBClassifier in Python
- Regression Example with XGBRegressor in Python
- Gradient Boosting Regression Example with GBM in R
- Gradient Boosting Regression Example in Python
- Text Classification Example with Keras LSTM in Python
- Sentiment Classification with NLTK Naive Bayes Classifier
- Understanding Word Embedding with Keras in Python
- One Hot Encoding Example in Python
- Sentiment Classification with Keras in Python
- Sentiment Classification Example in Python
- QDA Classification with R
- Regression Example with K-Nearest Neighbors in Python
- Sentiment Analysis Example with ML.NET in C#
- Classification Example with Stacking Method in R
- Regression Example with MXNet in R
- Classification with Voting Classifier in Python
- Classification with Adaboost Classifier in Python
- Classification with Bagging Classifier in Python
- How to create a ROC curve in R
- Gradient Boosting Classification Example in Python
- Precision, Recall, Specificity, Prevalence, Kappa, F1-score check with R
- Regression Model Accuracy (MAE, MSE, RMSE, R-squared) Check in R
- Classification with sklearn Decision Trees Classifier
- Sentiment Analysis Example in R
- Fully-connected RNN with Keras layer_simple_rnn in R
- Regression Example with Keras LSTM Networks in R
- Regression Example with Keras in R
- Regression Example with Keras in Python
- Support Vector Regression Example in Python

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