Difference between revisions of "TensorBoard"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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[http://www.youtube.com/results?search_query=tensorboard YouTube search...]
 
[http://www.youtube.com/results?search_query=tensorboard YouTube search...]
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* [http://www.tensorflow.org/guide/graph_viz TensorBoard: Graph Visualization]
 
* [http://www.tensorflow.org/guide/graph_viz TensorBoard: Graph Visualization]
 
* [http://playground.tensorflow.org Neural Networks Playground]
 
* [http://playground.tensorflow.org Neural Networks Playground]
* [[Visualization]]
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* [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]
 
* [[Colaboratory]] (Colab)
 
* [[Colaboratory]] (Colab)
 
* [[Data Flow Graph (DFG)]]
 
* [[Data Flow Graph (DFG)]]
 
* [[TensorWatch]]
 
* [[TensorWatch]]
 
* [[Principal Component Analysis (PCA)]] ...linear
 
* [[Principal Component Analysis (PCA)]] ...linear
* [[Embedding]]
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 
** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] ...non-linear
 
** [[T-Distributed Stochastic Neighbor Embedding (t-SNE)]] ...non-linear
 
* [[Graphical Tools for Modeling AI Components]]
 
* [[Graphical Tools for Modeling AI Components]]

Latest revision as of 09:50, 13 September 2023

YouTube search... ...Google search

In machine learning, to improve something you often need to be able to measure it. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. provides the visualization and tooling needed for machine learning experimentation:

  • Tracking and visualizing metrics such as loss and accuracy
  • Visualizing the model graph (ops and layers)
  • Viewing histograms of weights, biases, or other tensors as they change over time
  • Projecting embeddings to a lower dimensional space
  • Displaying images, text, and audio data
  • Profiling TensorFlow programs