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

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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: Not available
  • Address: Granada, Spain
  • Email: gabriel@jimpere.com
  • Spoken Languages: English
  • Github: GabriJP
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Experience
Education
Skills
Experience
Technology Developer - SDG Ibérica
2019 - act.

Data Scientist and B2B Developer

Education
Engineering Degree - University of Las Palmas de Gran Canaria
2013 - 2017

Degree in Computer Science Engineering - Computing

Master's Degree - University of Granada
2017 - 2018

Master's 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

my projects

works
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Contact

get in touch

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

Degree 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

Master's Degree Final Dissertation

  • Studies : Master's 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