Difference between revisions of "Writing/Publishing"

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= Publishing =
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* [[Case Studies]]
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** [http://deepindex.org/#Creative Creative]
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*** [[Music]]
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*** [[Art]]
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*** [[Photography]]
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*** [[Video]] & Movie Entertainment
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*** [[Writing]]
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* [[Capabilities]]
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** [[Embedding]]: [[AI-Powered Search|Search]]  ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
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** [[Video]] ... [[Generated Image]] ... [[Colorize]] ... [[Image/Video Transfer Learning]]
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** [[End-to-End Speech]] ... [[Synthesize Speech]] ... [[Speech Recognition]]
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* [[Synthesize Speech]]
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* [[Generative Pre-trained Transformer (GPT)]]
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* [[Wikis]]
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* [[Generative AI]]  ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[BingAI]] ... [[You]] ...[[Google]]'s [[Bard]]
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* [http://deckrobot.com/ DeckRobot]  will design hundreds of your PowerPoint slides in seconds not hours.
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As a publisher, you are under increasing pressure to produce engaging content. Audience engagement is an always moving target, and there is more competition for attention every day. You cannot consistently engage your audience purely on intuition or guesswork. So, they turn to technology to help them understand their audience, and ultimately, the content that drives, and will drive, the most engagement. If a publisher can gain that valuable insight, they can generate engagement on-demand, and increase their readership. [http://www.native.ai/content-analytics-for-publishers-guide Analytics for Publishers: The Ultimate Guide to Content Analytics | NativeAI]
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= <span id="Model Publishing"></span>Model Publishing =
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* [[Git - GitHub and GitLab]]  ...a [[development]] platform inspired by the way you work. From open source to business, you can host and review code, manage projects, and build software alongside 50 million developers.
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** [http://github.com/search?q=artificial+intelligence&ref=searchresults&type=Repositories GitHub AI]
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* [http://resources.wolframcloud.com/NeuralNetRepository/ Wolfram Neural Net Repository | Wolfram]  ...public resource that hosts an expanding collection of trained and untrained neural network models, suitable for immediate evaluation, training, visualization, transfer learning and more.
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* [[Algorithm Administration]]
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** [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]]
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<b>[[TensorFlow]] Hub: Making model discovery easy (TF Dev Summit '20)
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</b><br>TF Hub is the main repository for ML models. This talk looks into all the new features and how you can use Hub in your model discovery journey. Sandeep Gupta - Product Manager  Resources: [[TensorFlow]] Hub → https://goo.gle/32XwUY9 GitHub → http://goo.gle/3cGLdFc  Neural style transfer → http://goo.gle/2VPlqEE  Text classification with [[TensorFlow]] Hub → http://goo.gle/2VQZHMp  Watch all [[TensorFlow]] Dev Summit 2020 sessions → http://goo.gle/TFDS20  Subscribe to the [[TensorFlow]] YouTube channel → https://goo.gle/TensorFlow
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<youtube>yogPjalnJ08</youtube>
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<b>Tech Talks 2019: Building Applications with the Neural Net Repository
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</b><br>Tuseeta Banerjee discusses BERT, GPT2, and curated neural net models using the Wolfram Neural Network Framework in Version 12 of the Wolfram Language.
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<youtube>2OT3AdlkjN0</youtube>
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<b>AWS CodeCommit | Concept | Demo:- Create, Clone, Commit, Push into Repository Using Git commands
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</b><br>Video will help us to understand the concept of [[Amazon]] AWS CodeCommit and how we can configure the repo along with performing different type of version control operations using Git commands. 
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<youtube>dbLu0r2eNZs</youtube>
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<b>Neural Networks in the Wolfram Language
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</b><br>In this first webinar of the three-part Machine Learning webinar series, learn how to use the built-in neural net framework and build a net model from scratch, as well as how to work with a ready-to-use model from the Neural Net Repository.
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Revision as of 17:44, 22 March 2023

Youtube search... ...Google search



Data Storytelling for Business

Language/Grammar Checkers

Youtube search... ...Google search

Use machine learning and Natural Language Processing (NLP) technology to find writing errors and offer suggestions; highlight redundant sentences, passive voice, adverbs, and repeated words, etc.

Tools:

Publishing

As a publisher, you are under increasing pressure to produce engaging content. Audience engagement is an always moving target, and there is more competition for attention every day. You cannot consistently engage your audience purely on intuition or guesswork. So, they turn to technology to help them understand their audience, and ultimately, the content that drives, and will drive, the most engagement. If a publisher can gain that valuable insight, they can generate engagement on-demand, and increase their readership. Analytics for Publishers: The Ultimate Guide to Content Analytics | NativeAI

Model Publishing

TensorFlow Hub: Making model discovery easy (TF Dev Summit '20)
TF Hub is the main repository for ML models. This talk looks into all the new features and how you can use Hub in your model discovery journey. Sandeep Gupta - Product Manager Resources: TensorFlow Hub → https://goo.gle/32XwUY9 GitHub → http://goo.gle/3cGLdFc Neural style transfer → http://goo.gle/2VPlqEE Text classification with TensorFlow Hub → http://goo.gle/2VQZHMp Watch all TensorFlow Dev Summit 2020 sessions → http://goo.gle/TFDS20 Subscribe to the TensorFlow YouTube channel → https://goo.gle/TensorFlow

Tech Talks 2019: Building Applications with the Neural Net Repository
Tuseeta Banerjee discusses BERT, GPT2, and curated neural net models using the Wolfram Neural Network Framework in Version 12 of the Wolfram Language.

AWS CodeCommit | Concept | Demo:- Create, Clone, Commit, Push into Repository Using Git commands
Video will help us to understand the concept of Amazon AWS CodeCommit and how we can configure the repo along with performing different type of version control operations using Git commands.

Neural Networks in the Wolfram Language
In this first webinar of the three-part Machine Learning webinar series, learn how to use the built-in neural net framework and build a net model from scratch, as well as how to work with a ready-to-use model from the Neural Net Repository.

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