Hi There! I'm Gabriel Jiménez Perera a computer scientist a data scientist a researcher

Resume

About Me

About Me

Resume
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Personal Information

I'm a Data Scientist & researcher based in Granada, Spain.
I have serious passion for new challenges, travelling and adapting to new environments with the advantage of passion.

  • First Name: Gabriel
  • Last Name: Jiménez Perera
  • Date of birth: 21 february 1995
  • Nationality: Spanish
  • Freelance: Available
  • Address: Granada, Spain
  • Email: gabriel@jimpere.com
  • Spoken Languages: English
  • Github: GabriJP
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Education
Skills
Education
Engineering Degree - University de Las Palmas de Gran Canaria
2013 - 2017

Degree in Computer Science Engineering - Computing

Masters Degree - Universidad de Granada
2017 - 2018

Masters Degree in Data Science and Computer Engineering

Skills
Python

R

Java

Keras

Tensorflow

Scikit Learn

2+

Years Experience

70+

Done Projects

1+

Years Research
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works

my projects

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Contact

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get in touch

Contact
Email
gabriel@jimpere.com
Address
Granada, Spain
Feel free to drop me a line

If you have any suggestion, project or even you want to say Hello.. please fill out the form below and I will reply you shortly.

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project

Final Dissertation

  • Studies : Degree
  • Date : 24/07/2017
  • Used Technologies : Python, Tensorflow, LSTM, MemN2N, DNC

This project proposes experimenting and comparing three artificial neural network models that have had quite accomplishment in natural language processing: LSTM (Long Short-Term Memory), MemN2N (model proposed by Facebook) and DNC (model proposed by Google). For this task, these optimized models have been adapted to a concrete scope, with the objective of comparing the results of each

Code
project

Masters Degree Final Dissertation

  • Studies : Masters Degree
  • Date : 25/09/2018
  • Used Technologies : R, Python, Matlab, Keras, Tensorflow, Scikit Learn

The present end of master's degree work proposes the use of medical image processing tools for feature extraction of cortical thickness and its subsequent analysis and classification through pattern recognition algorithms. Resampling and feature extraction techniques will be used to compensate the effect of small sample size in statistical validation systems based on boosting, bagging, RFs, SVM, ANNs, etc.

Code