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@thangqn
Membro desde 6 de maio de 2013
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thangqn

On-line Offline
I love Computer Science and Mathematics.
$10 USD/hr
2 comentários
1.3
  • 67%Trabalhos concluídos
  • 100%No Orçamento
  • 100%Pontualmente
  • N/ATaxa de Recontratação

Portfólio

Comentários recentes

  • imagem de Duc Chung T. C Programming $12.00 USD

    “Good employee, good knowledge in C programming, lack of skill to follow up with the project. Will re-hire but will consider about the quality of the work as well.”

  • imagem de mustafav C++ Reading and doing some Calculations from the text file - repost 2 $35.00 USD

    “This guy is so helpful and full of [login to view URL] send the codes before releasing money . He is doing his job very well. He is an expert on C++ and Java”

Experiência

C/C++ Programmer

Oct 2007

C/C++ Programmer

Educação

Engineer

2008 - 2013 (5 years)

Qualificações

Algorithms: Design and Analysis, Part 1 (2013)

Coursera

A FREE ONLINE COURSE PROVIDED BY STANFORD UNIVERSITY THROUGH COURSERA INC. This is an undergraduate course on the design and analysis of algorithms. The main topics are: asymptotic analysis, divide and conquer algorithms, sorting and searching, basic randomized algorithms, graph search, shortest paths, heaps, search trees, and hash tables.

Algorithms: Design and Analysis, Part 2 (2013)

Coursera

A FREE ONLINE COURSE PROVIDED BY STANFORD UNIVERSITY THROUGH COURSERA INC. This course covers greedy algorithms, including applications to minimum spanning trees and Huffman codes; dynamic programming, including applications to sequence alignment and shortest-path problems; and exact and approximation algorithms for NP-complete problems.

Machine Learning (2013)

Coursera

A FREE ONLINE COURSE PROVIDED BY STANFORD UNIVERSITY THROUGH COURSERA INC. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

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