Difference between revisions of "Google"

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* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 
* [https://ai.google/tools/ Google's Tools and Resources]
 
* [https://ai.google/tools/ Google's Tools and Resources]
 
* [[Vertex AI]]
 
* [[Vertex AI]]
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** [[TensorBoard]]  ...TensorFlow's visualization toolkit
 
** [[TensorBoard]]  ...TensorFlow's visualization toolkit
 
** [https://www.tensorflow.org/hub TensorFlow Hub] is a library for reusable machine learning modules that you can use to speed up the process of training your model. A [[TensorFlow]] module is a reusable piece of a [[TensorFlow]] graph. With transfer learning, you can use [[TensorFlow]] modules to preprocess input feature vectors, or you can incorporate a [[TensorFlow]] module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
 
** [https://www.tensorflow.org/hub TensorFlow Hub] is a library for reusable machine learning modules that you can use to speed up the process of training your model. A [[TensorFlow]] module is a reusable piece of a [[TensorFlow]] graph. With transfer learning, you can use [[TensorFlow]] modules to preprocess input feature vectors, or you can incorporate a [[TensorFlow]] module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]:
* * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]:
 
 
** [https://blog.google/technology/ai/lamda/  LaMDA (Language Model for Dialogue Applications): our breakthrough conversation technology]  
 
** [https://blog.google/technology/ai/lamda/  LaMDA (Language Model for Dialogue Applications): our breakthrough conversation technology]  
 
** [https://github.com/pair-code/lit Language Interpretability Tool (LIT) | Google - GitHub]
 
** [https://github.com/pair-code/lit Language Interpretability Tool (LIT) | Google - GitHub]

Revision as of 06:19, 21 May 2023

YouTube ... Quora ...Google search ...Google News ...Bing News



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AI Infrastructure on GCP (Cloud Next ‘19 UK)
Google Cloud’s AI-optimized infrastructure is setting performance records. From a wide range of our GPU accelerators, to our custom-built supercomputers, Cloud TPU Pods, GCP offers a modern infrastructure for your ML workflows. Learn how you to get started with these accelerators through GCP products and services.

Cloud AI & ML (Google Cloud Talks by DevRel)
Our high-quality, scalable, continuously improving, and fully managed AI services put you in a position to innovate faster and more efficiently than the competition. Join our Developer Advocate team to get a comprehensive view of Google Cloud AI platform and solutions and learn how it can help turn your ideas to deployment rapidly and efficiently. Learn more about Google Cloud AI Platform: https://cloud.google.com/ai-platform

The Path From Cloud AutoML to Custom Model (Cloud Next '19)

by Sara Robinson

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The Path From Cloud AutoML to Custom Model (Cloud Next '19)
What comes after AutoML? You've created some models in Cloud AutoML, and they've been useful. But you want to see if there's some room for more improvement and customization. Let's see how to start building custom models and deploy them in production. You'll learn how to take advantage of state-of-the-art models, all while advancing your understanding of your data pipelines and machine learning. Cloud AutoML to Custom Model → https://bit.ly/2UhnfL7 Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform Speaker(s): Yufeng Guo, Sara Robinson

Building custom models in AutoML (DevFest 2019)
Kevin Nelson (@knelsoncloud), Developer Advocate for Google Cloud, discusses Machine Learning and building custom models in AutoML. Watch Kevin demonstrate building custom vision and structured data models during this talk.

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DevFest on demand 2019 → https://goo.gle/372VJ69 Subscribe to Google Developer Groups → https://goo.gle/GDevGroups