Data Science

Data science. Short:

  • Technical qualifications: Natural Language Processing, Fine-tuning state of the art models, Computer vision.
  • Transfer learning: State of the art models, Deep learning frameworks, Detection/recognition frameworks, Machine Learning, Forecasting modeling.

Technology stack

Our data science team can perform wide range of tasks. They have strong knowledge at such solutions:

Technical qualifications

  • Natural Language Processing (NLP): Data preparation, sentiment and similarity analysis, topic modeling, keywords extraction.
  • Fine-tuning state of the art models: GPT-2, BART, BERT, ALBERT, DistilBERT, Grover
  • Development of custom metrics for semantic analyses of generated text
  • Deploying models as high load APIs on cloud infrastructure
  • Computer vision (CV): Object detection, object classification, face recognition, emotion recognition, segmentation, pose recognition.
  • Implementation of detection, classification, and recognition algorithms on static images and real-time video streaming data

Transfer learning

  • State of the art models: MobileNetV3, VGG16/19, ResNet50, RefineNet
  • Deep learning frameworks: Tensorflow, Keras
  • Detection/recognition frameworks: SSD
  • Machine Learning (ML): ensemble methods (XGBoost, Random Forest, LightGBM), model validation and training (boosting, cross-validation), hyperparameters fine-tuning, feature engineering
  • Forecasting modeling: time series trend and seasonality modeling (Fbprophet, ARIMA, SARIMA)
  • Front-end Data Science framework: Streamlit
  • Programming languages and frameworks: Python, Django, Node.js
  • Python data science libraries: NumPy, Pandas, SciPy, SKLearn, TensorFlow, spaCy
  • Fast search ANN: Annoy.

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