User talk:149.54.6.166

May 2022
Hello. This is a message to let you know that one or more of your recent contributions, such as the edit(s) you made to Data, did not appear to be constructive and have been reverted. Please take some time to familiarise yourself with our policies and guidelines. You can find information about these at our welcome page which also provides further information about contributing constructively to this encyclopedia. If you only meant to make test edits, please use the sandbox for that. If you think I made a mistake, or if you have any questions, you may leave a message on my talk page. Thank you. DoebLoggs (talk) 09:22, 18 May 2022 (UTC)

Please refrain from making unconstructive edits to Wikipedia, as you did at Research. Your edits appear to constitute vandalism and have been reverted. If you would like to experiment, please use the sandbox. Repeated vandalism may result in the loss of editing privileges. Thank you. Nythar (talk) 09:35, 18 May 2022 (UTC)

Please stop your disruptive editing. If you continue to vandalize Wikipedia, as you did at Research, you may be blocked from editing. Nythar (talk) 10:02, 18 May 2022 (UTC)

My topic
Deep learning is one of the major subfield of machine learning framework. Machine learning is the study of design of algorithms, inspired from the model of human brain. Deep learning is becoming more popular in data science fields like robotics, artificial intelligence(AI), audio & video recognition and image recognition. Artificial neural network is the core of deep learning methodologies. Deep learning is supported by various libraries such as Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of popular deep learning libraries like TensorFlow, Theano, etc., for creating deep learning models.

Overview of Keras Keras runs on top of open source machine libraries like TensorFlow, Theano or Cognitive Toolkit (CNTK). Theano is a python library used for fast numerical computation tasks. TensorFlow is the most famous symbolic math library used for creating neural networks and deep learning models. TensorFlow is very flexible and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as Python, C#, C++ or standalone machine learning toolkits. Theano and TensorFlow are very powerful libraries but difficult to understand for creating neural networks.

Keras is based on minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano. Keras is designed to quickly define deep learning models. Well, Keras is an optimal choice for deep learning applications.

Features Keras leverages various optimization techniques to make high level neural network API easier and more performant. It supports the following features −

Consistent, simple and extensible API.

Minimal structure - easy to achieve the result without any frills.

It supports multiple platforms and backends.

It is user friendly framework which runs on both CPU and GPU.

Highly scalability of computation.

Benefits Keras is highly powerful and dynamic framework and comes up with the following advantages −

Larger community support.

Easy to test.

Keras neural networks are written in Python which makes things simpler.

Keras supports both convolution and recurrent networks.

Deep learning models are discrete components, so that, you can combine into many ways. 149.54.6.166 (talk) 10:11, 24 May 2022 (UTC)

Hello. This is a message to let you know that one or more of your recent contributions, such as the edit(s) you made to Research proposal, did not appear to be constructive and have been reverted. Please take some time to familiarise yourself with our policies and guidelines. You can find information about these at our welcome page which also provides further information about contributing constructively to this encyclopedia. If you only meant to make test edits, please use the sandbox for that. If you think I made a mistake, or if you have any questions, you may leave a message on my talk page. Thank you. Adakiko (talk) 09:02, 25 May 2022 (UTC)

Welcome!
Hello! I noticed your contributions to Research&#32;and wanted to welcome you to the Wikipedia community. I hope you like it here and decide to stay. You are welcome to edit anonymously; however, creating an account is free and has several benefits (for example, the ability to create pages, upload media and edit without one's IP address being visible to the public).

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Happy editing! AdrianHObradors (talk) 11:43, 1 June 2022 (UTC)

June 2022
Hello, I'm AdrianHObradors. An edit that you recently made to Research seemed to be a test and has been reverted. If you want to practice editing, please use the sandbox. If you think a mistake was made, or if you have any questions, you can leave me a message on my talk page. ''Hi! Your editions to research are adding information that is already there, and it is breaking the format. I recommend you preview your changes before you publish them, and you can read more about editing Wikipedia above. Thanks!'' AdrianHObradors (talk) 11:47, 1 June 2022 (UTC)