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AutoKeras

Accessible AutoML for deep learning.

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+ 29

Top financial contributors

Individuals

1
Dekrit Gampamole

$460 USD since Aug 2019

2
Michael wiegand

$300 USD since Aug 2020

3
nishio kei

$215 USD since Mar 2019

4
David

$200 USD since Aug 2021

5
Yi-Lin Tung

$100 USD since Nov 2018

6
Jonny Hinojosa

$100 USD since Sep 2019

7
Miaosen Zhang

$75 USD since Jan 2020

8
Sanjay Basu

$45 USD since Aug 2019

9
Tyler Evans

$35 USD since Aug 2019

10
Matthew Jurewicz

$30 USD since Feb 2019

Organizations

1
black.ai

$495 USD since Sep 2019

2
Triplebyte

$135 USD since Aug 2019

3
Hosted.nl

$110 USD since Nov 2018

4
Salesforce

$25 USD since Dec 2019

5
Wechaty

$25 USD since Mar 2020

6
Alqamoos

$10 USD since Jan 2019

7
SETScholars

$10 USD since Aug 2019

8
Alphabotics

$5 USD since Dec 2018

AutoKeras is all of us

Our contributors 34

Thank you for supporting AutoKeras.

black.ai

$495 USD

Great project - looking forward to the 1.0 rele...

Michael wiegand

backer

$300 USD

nishio kei

backer

$215 USD

i'm glad to be Auto-Keras's 'backer'

David

$200 USD

Triplebyte

$135 USD

Hosted.nl

backer

$110 USD

Yi-Lin Tung

$100 USD

Jonny Hinojosa

$100 USD

Gig'em - '89

Sanjay Basu

backer

$45 USD

This is an amazing Open Source AutoML software....

Budget


Transparent and open finances.

GitHub Codespaces

from Haifeng Jin to AutoKeras
-$9.41 USD
Paid
Reimbursement #193387

GitHub codespaces

from Haifeng Jin to AutoKeras
-$8.87 USD
Paid
Reimbursement #172407
-$48.11 USD
Paid
Reimbursement #167421
$
Today’s balance

$1,957.30 USD

Total raised

$2,065.34 USD

Total disbursed

$108.04 USD

Estimated annual budget

--.-- USD

About


Auto-Keras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.

Our team