Lakshmi N. Profile

探索

4.7

(14条评论)

您正在关注

关注用户错误。

该用户不允许他人关注他们。

您已经关注了这个用户。

您的会员计划仅允许0个关注。升级在此。

成功取消关注

取消关注用户错误

您已成功推荐

推荐用户错误。

出问题了，请刷新页面并重新尝试。

电子邮箱已成功验证。

$20 USD / 小时

hyderabad,

india

$20 USD / 小时

目前这里是11:35 下午

十月 19, 2012已加入

1 建议

Lakshmi N.

@lnarayanan90

4.7

(14条评论)

4.2

86%

$20 USD / 小时

hyderabad,

india

$20 USD / 小时

60%

工作已完成

91%

在预算内

44%

按时

重复雇用率

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.

查看所有Python Developers在India

联系Lakshmi N.询问您的工作详情

登录并通过聊天讨论所有细节。

文件夹

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

变更已保存

展示16中的1-5条评论

筛选评论规则：

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更多

Closed User

@gwanharridomw

•

3 年前

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

•

5 年前

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

•

6 年前

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更多

Zied S.

@deeperdragon

•

6 年前

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更多

Caleb W.

@calebbaywatch

•

6 年前

经验

IIIT, Hyderabad

5月 2012 - 现在

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

6月 2011 - 3月 2012 (9 个月, 1 天)

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