DeepCTR download for Windows

This is the Windows app named DeepCTR whose latest release can be downloaded as v0.9.3.zip. It can be run online in the free hosting provider OnWorks for workstations.

 
 

Download and run online this app named DeepCTR with OnWorks for free.

Follow these instructions in order to run this app:

- 1. Downloaded this application in your PC.

- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 3. Upload this application in such filemanager.

- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.

- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 6. Download the application and install it.

- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.

Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.

SCREENSHOTS:


DeepCTR


DESCRIPTION:

DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based techniques have been widely used in CTR prediction task. The data in CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Since DNN are good at handling dense numerical features,we usually map the sparse categorical features to dense numerical through embedding technique.



Features

  • CCPM (Convolutional Click Prediction Model)
  • PNN (Product-based Neural Network)
  • FNN (Factorization-supported Neural Network)
  • MLR(Mixed Logistic Regression/Piece-wise Linear Model)
  • NFM (Neural Factorization Machine)
  • DCN (Deep & Cross Network)


Programming Language

Python


Categories

Machine Learning, Package Managers

This is an application that can also be fetched from https://sourceforge.net/projects/deepctr.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.



Latest Linux & Windows online programs


Categories to download Software & Programs for Windows & Linux