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$30 USD / hora
Bandera de INDIA
pondicherry, india
$30 USD / hora
Aquí son las 10:50 a. m.
Se unió el febrero 7, 2016
0 recomendaciones

Dinesh R.

@dineshrajavel

 4,4 (2 comentarios)
3,1
3,1
100%
100%
$30 USD / hora
Bandera de INDIA
pondicherry, india
$30 USD / hora
100 %
Trabajos finalizados
100 %
Dentro del presupuesto
59 %
A tiempo
50 %
Tasa de recontratación

Machine Learning, Deep Learning, AI, IoT Projects.

I am an professional programmer with very good academic background completed my master's in Indian Institute of Technology Guwahati. Currently pursuing my research work in Indian Institute of Technology Kharagpur in the domain of Internet of Things, Artificial Intelligence and Machine Learning. Myself dedicated to learn and solve the Algorithmic and Data Structure problems . I dedicated my self to solve various machine learning project such object detection and facial recognition using RPI. I have 4 years of experience in software engineer.
Freelancer Python Developers India
Ver todo Python Developers en India

Contacta Dinesh R. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Portafolio

7779900
- Understand the major technology trends driving Deep Learning. - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture
Neural Networks and Deep Learning
7779850
I have learnt how to build models for natural language, audio, and other sequence data. Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. And able to apply sequence models to natural language problems, including text synthesis. And able to apply sequence models to audio applications, including speech recognition and music synthesis.
Sequence Models
7779568
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow.
Improving Deep Neural Networks: Hyperparameter tuning.
7779490
I have learned how to build a successful machine learning project. Understand how to diagnose errors in a machine learning system, and able to prioritise the most promising directions for reducing error. Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Know how to apply end-to-end learning, transfer learning, and multi-task learning
Structural Machine Learning Projects
7779900
- Understand the major technology trends driving Deep Learning. - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture
Neural Networks and Deep Learning
7779850
I have learnt how to build models for natural language, audio, and other sequence data. Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. And able to apply sequence models to natural language problems, including text synthesis. And able to apply sequence models to audio applications, including speech recognition and music synthesis.
Sequence Models
7779568
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow.
Improving Deep Neural Networks: Hyperparameter tuning.
7779490
I have learned how to build a successful machine learning project. Understand how to diagnose errors in a machine learning system, and able to prioritise the most promising directions for reducing error. Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Know how to apply end-to-end learning, transfer learning, and multi-task learning
Structural Machine Learning Projects

Comentarios

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Mostrando 1 - 2 de 2 reseñas
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5,0
$200,00 USD
Project 27532488 has been deleted
Hard and thorough worker
A
Closed User
@AnwerUK
•
hace 3 años
3,6
$140,00 USD
Project 27324588 has been deleted
Hard worker. But needs supervision
A
Closed User
@AnwerUK
•
hace 3 años

Experiencia

Software Engineer

Samsung Data System
may 2015 - dic 2015 (7 meses, 1 día)
As a

Educación

M.Tech

Indian Institute of Technology, Guwahati, India 2013 - 2015
(2 años)

Contacta Dinesh R. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Verificaciones

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Facebook conectado

Principales habilidades

Python 1
C++ Programming 1
Mathematics 1
C Programming
Java

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