Skip to content

Latest commit

 

History

History
87 lines (63 loc) · 4.73 KB

README.md

File metadata and controls

87 lines (63 loc) · 4.73 KB
Raw

My experience

Algorithms

The list is to give an idea of the type of algorithms, which I have worked.

NLP

  • Neural network (Bidirectional LTMS-Attention-Bidirectional LTMS) - Machine translation
  • Neural network (CNN-GRU) - Trigger word detection
  • Neural network (RNN-LTMS) - "Jazz music" generation
  • Neural network (Embedding-LSTM) - Text to emoji

Image recognition

  • Neural network - Cat Images classification
  • Deep neural network (DNN) - Cat images classification
  • Convolutional neural Network (CNN) - Cat images classification
  • Neural network - "Thump up" image classification
  • Convolutional neural network (CNN) - "Happy face" images classification
  • Convolutional neural network (CNN) - "Thump up" image classification
  • Convolutional neural network - "Digit" image recognizer. Tensorflow version. (Link)
  • Convolutional neural network - "Digit" image recognizer. Pytorch version. (Link)

Linear regression

  • Linear regression (LightGBM) - "Housing prices" estimator. (Link)
  • Linear regression (XGBoost) - "Housing prices" estimator. (Link)

Classification & Segmentation

  • SVM - "Human cell"-classification
  • k-means clustering - Customer segmentation
  • Density based(DBSCAN) clustering - "Weather stations" segmentation
  • KNN/Decision Tree/SVM/Logistic regression - "Loan application"
  • k-means clustering with Foursquare - Customer segmentation

Recommender systems

  • Recommender System as Content based - "Movies"
  • Recommender System as Collaborative Filtering - "Movies"

Applications

I add here few examples of sample applications, which I have worked and are available publicly.

Libraries

  • NLP: Open AI API, HuggingFace, SpaCy, NLTK, regex
  • MLOps: MLflow
  • Deep learning: Tensorflow/Keras, Pytorch
  • Xgboost, LightGBM
  • Visualization libraries experience: Dash, Seaborn, Matplotlib, Bokeh
  • Web frameworks: * Flask example and integrating it with SQLite, Jinja2 and Bokeh-visualizations.
  • Data manipulation: Numpy, Pandas, JSON - example
  • Other libraries: Folium, Geopy, Foursquare-location data with k-means clustering - Neighborhood segmentation

Conceptual knowledge

The below articles are available in Medium. This entry is especially useful, in case you are interested in the latest NLP research.