Paolo Tamagnini

Data scientist specialized in guided analytics applications

paolotamag [at] gmail [dot] com

Data scientist with research experience, fond of data visualization and machine learning, currently working for KNIME in Berlin.

Keywords: guided analytics; visual analytics; machine learning; data science; big data; Python; JavaScript; d3.js; KNIME; AWS; ...

In May 2018 I moved to Berlin to work as a data scientist in the evangelism team of KNIME. My work at KNIME is variegated, but it is particularly focused on guided analytics and automated machine learning. Since May 2021 I have been leading the Verified Components team of data scientists, releasing data science libraries, functions and tools via reusable and reliable KNIME workflows.

Between November 2017 and February 2018 I worked on an open-source python library called partial_dependence in collaboration with Josua Krause and Enrico Bertini.

In October 2017 I graduated at the Master (MSc) of Data Science from Sapienza University of Rome and my thesis is available here.

In August 2016 I wrote my thesis in the research team of Enrico Bertini, associate professor at New York University, Tandon School of Engineering.

Projects

• Rivelo - visual analytics tool for machine learning explanations.
• partial_dependence - python package for machine learning visualization.

Certifications

• Deep Learning

  a 5-course specialization by deeplearning.ai on Coursera.
  Specialization Certificate earned on April 4, 2018.

  
  

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  • Neural Networks and Deep Learning
    
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    topics: logistic regr., deep and shallow NNs, back prop., ..
    tools: python, numpy
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    
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    topics: L2 and Dropout Regularization, mini-batch gradient descent, Adam optimization algorithm, Batch Norm, ..
    tools: python, numpy, TensorFlow
  • Structuring Machine Learning Projects
    
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    topics: evaluation metric, error analysis, mismatched data distributions, transfer and multi-task learning, ..
  • Convolutional Neural Networks
    
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    topics: Computer Vision, CNNs, Conv. and Pooling layers, ResNets, Inception Nets, object detection, YOLO Algorithm, face recognition ..
    tools: python, TensorFlow, Keras, ..
  • Sequence Models
    
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    topics: NLP, RNNs, GRUs, LSTMs, word embeddings, speech recognition, attention models, ..
    tools: python, Keras, Word2Vect, GloVe, ..