Lakshmi N. Profile

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4,7

(14 comentarios)

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$20 USD / hora

hyderabad,

india

$20 USD / hora

Aquí son las 9:42 p. m.

Se unió el octubre 19, 2012

1 Recomendación

Lakshmi N.

@lnarayanan90

4,7

(14 comentarios)

4,2

86%

$20 USD / hora

hyderabad,

india

$20 USD / hora

60 %

Trabajos finalizados

91 %

Dentro del presupuesto

44 %

A tiempo

3 %

Tasa de recontratación

Love Robotics, Computer Vision, Embedded, Machine Learning, Android , Labview, any programming!

I primarily focus on perfection, or create and do things by giving my best! My motto, Live and do your best every second :). My passionate interests are Embedded, Robotics, Machine Vision, NLP, ML and Neural networks. I also work with android app development. Languages skilled : 1. python (theano/tensorflow/keras/caffe/sklearn/pandas/numpy/scipy/django/beautifulsoup4/selenium) 2. Matlab 3. C/c++ Packages : 1. Tesseract 2. Opencv 3. ocropy 4. ALPR Applications: 1. OCR 2. ANPR - Automatic Number Plate Recognition 3. Object Recognitions 4. Image classifications 5. Forecasts and prediction models. 6. Stock and Forex forecasts / predictive analysis.

Ver todo Python Developers en India

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Portafolio

7296063

Code for Success Window Prediction

The objective of this project was to identify the Success window of Accident mitigation strategies in Nuclear power plants

Success Window prediction for Accident mitigation strategies

4670107

Code files For CNN analysis

1. the goal of the project is to make use of two custom layers,from publications - Accept Reject Pooling and Stochastic Pooling layer and compare its performances.

2. The layers were implemented into Keras models and used to train MNIST, CIFAR 10 and CIFAR 100 datasets.

3. The implementation was done with keras with tensorflow backend and jupyter notebook.

CNN analysis for Accept_Reject Pooling and Stochastic Pool

1. The objective of this project was to create a Neural Netowkr, without using packages of python and to write the code from scratch to gain a deep understanding of neural networks.

2. the created NN was used to train and detect credit card fraud transactions from available open source databases.

Credit Card Fraud Detection using Neural Networks

A star puzzle in python

7296063

Code for Success Window Prediction

Success Window prediction for Accident mitigation strategies

4670107

Code files For CNN analysis

CNN analysis for Accept_Reject Pooling and Stochastic Pool

Credit Card Fraud Detection using Neural Networks

A star puzzle in python

Comentarios

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Mostrando 1 - 5 de 16 reseñas

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1,0

$250,00 USD

Project for Lakshmi N.

Didn't complete project. Never hire.

Python

Matlab and Mathematica

Machine Learning (ML)

Face Recognition

+1 más

Closed User

@gwanharridomw

•

hace 3 años

0,0

$188,00 USD

create a model DL movidius compatible to read caracter licence plate

The project is not completed according to the dispute

Python

Machine Learning (ML)

Raspberry Pi

Frederic F.

@restemo

•

hace 5 años

3,0

$100,00 USD

Project 17219302 has been deleted

Unfortunately not able to proceed because Lakshmi had some health issues. Mutually agreed to cancel.

Justin T.

@vw777176vw

•

hace 6 años

1,0

$450,00 USD

Project for Lakshmi N. -- 2

He is unprofessional, he tells me okay in the beginning but he is not able to do my project. it loses me a lot of time .... is not not recommended

Python

Matlab and Mathematica

Algorithm

Machine Learning (ML)

+1 más

Zied S.

@deeperdragon

•

hace 6 años

4,6

$255,00 USD

Project for Lakshmi N.

Lakshmi has been great to work with. I would definitely hire again!

Python

Matlab and Mathematica

Algorithm

Machine Learning (ML)

+1 más

Caleb W.

@calebbaywatch

•

hace 6 años

Experiencia

IIIT, Hyderabad

may 2012 - Presente

Research in Computer Vision, good matlab skills, audit and online courses in Machine Learning and Neural networks. Localization of Robot using vision and epipolar geometry. Worked side by side with India's top robotics Professors.

Soliton Technologies

jun 2011 - mar 2012 (9 meses, 1 día)

Master of Labview, faced tight deadlines, direct interaction with TI scientists on device requirements, and progressive learning of good programming and exceptional Labview skills.