Difference between revisions of "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]] | * [[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]] | ||
| + | * [[Algorithm Administration#Hyperparameter|Hyperparameter]]s | ||
| + | * [[Predictive Analytics]] ... [[Operations & Maintenance|Predictive Maintenance]] ... [[Forecasting]] ... [[Market Trading]] ... [[Sports Prediction]] ... [[Marketing]] ... [[Politics]] ... [[Excel#Excel - Forecasting|Excel]] | ||
| + | * [[Graph]] | ||
| + | ** [[Data Flow Graph (DFG)]] | ||
| + | ** [[Graphical Tools for Modeling AI Components]] | ||
| + | * [https://towardsdatascience.com/the-simpsons-meets-data-visualization-ef8ef0819d13 The Simpsons meets Data Visualization | Adam Reevesman -Towards Data Science] | ||
| + | * [[Robotics]] ... [[Transportation (Autonomous Vehicles)|Vehicles]] ... [[Autonomous Drones|Drones]] ... [[3D Model]] ... [[Point Cloud]] | ||
| + | * [[Simulation]] ... [[Simulated Environment Learning]] ... [[Minecraft]]: [[Minecraft#Voyager|Voyager]] | ||
| + | * [[Data Science]] | ||
| + | ** [[Data Governance]] | ||
| + | *** [[Benchmarks]] | ||
| + | *** [[Data Preprocessing]] | ||
| + | **** [[Feature Exploration/Learning]] | ||
| + | **** [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]] | ||
| + | *** [[Bias and Variances]] | ||
| + | *** [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] | ||
| + | **** [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]] | ||
| + | **** [[Datasets]] | ||
| + | *** [[Privacy]] in Data Science | ||
| + | *** [[Data Interoperability]] | ||
* Tools: | * Tools: | ||
| + | ** [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | ||
** [[Python#Visualization with Python |Visualization with Python]] | ** [[Python#Visualization with Python |Visualization with Python]] | ||
** [https://ai.google/research/teams/brain/pair Google: People + AI Research (PAIR)] | ** [https://ai.google/research/teams/brain/pair Google: People + AI Research (PAIR)] | ||
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** [https://explosion.ai/demos/displacy displaCy] | ** [https://explosion.ai/demos/displacy displaCy] | ||
* [https://www.kdnuggets.com/2019/01/five-best-data-visualization-libraries.html The Five Best Data Visualization Libraries | Lio Fleishman, Sisense] | * [https://www.kdnuggets.com/2019/01/five-best-data-visualization-libraries.html The Five Best Data Visualization Libraries | Lio Fleishman, Sisense] | ||
| − | ** [[ | + | ** [[JavaScript#D3.js|D3.js]] |
** [https://uber.github.io/react-vis/documentation/welcome-to-react-vis React-vis] | ** [https://uber.github.io/react-vis/documentation/welcome-to-react-vis React-vis] | ||
** [https://www.chartjs.org/ Chart.js] | ** [https://www.chartjs.org/ Chart.js] | ||
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* [https://www.infoq.com/presentations/ml-dl-visualization Understanding ML/DL Models using Interactive Visualization Techniques | Chakri Cherukuri] | * [https://www.infoq.com/presentations/ml-dl-visualization Understanding ML/DL Models using Interactive Visualization Techniques | Chakri Cherukuri] | ||
* [https://machinelearningmastery.com/data-visualization-methods-in-python/ A Gentle Introduction to Data Visualization Methods in Python | Jason Brownlee - Machine Learning Mastery] | * [https://machinelearningmastery.com/data-visualization-methods-in-python/ A Gentle Introduction to Data Visualization Methods in Python | Jason Brownlee - Machine Learning Mastery] | ||
| − | * [ | + | * [https://virtualitics.com/ Virtualitics] ... Get more from your data and your team with AI-powered data exploration ... from Descriptive to [[Prescriptive Analytics | Prescriptive]] |
| − | + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] | |
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* [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | * [[(Deep) Convolutional Neural Network (DCNN/CNN)]] | ||
* [https://www.evolvingai.org/files/mfv_icml_workshop_16.pdf Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks. Visualization for Deep Learning workshop | Nguyen A, Yosinski J, Clune J], 2016 | * [https://www.evolvingai.org/files/mfv_icml_workshop_16.pdf Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks. Visualization for Deep Learning workshop | Nguyen A, Yosinski J, Clune J], 2016 | ||
* [https://www.evolvingai.org/files/2016-VelezClune-SRC.pdf Identifying core functional networks and functional modules within artificial neural networks via subsets regression | Velez R, Clune J], 2016 | * [https://www.evolvingai.org/files/2016-VelezClune-SRC.pdf Identifying core functional networks and functional modules within artificial neural networks via subsets regression | Velez R, Clune J], 2016 | ||
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* [https://bookdown.org/max/FES/exploratory-visualizations.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 4 Exploratory Visualizations | Max Kuhn and Kjell Johnson] | * [https://bookdown.org/max/FES/exploratory-visualizations.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 4 Exploratory Visualizations | Max Kuhn and Kjell Johnson] | ||
Latest revision as of 08:20, 16 June 2024
YouTube ... Quora ...Google search ...Google News ...Bing News
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Hyperparameters
- Predictive Analytics ... Predictive Maintenance ... Forecasting ... Market Trading ... Sports Prediction ... Marketing ... Politics ... Excel
- Graph
- The Simpsons meets Data Visualization | Adam Reevesman -Towards Data Science
- Robotics ... Vehicles ... Drones ... 3D Model ... Point Cloud
- Simulation ... Simulated Environment Learning ... Minecraft: Voyager
- Data Science
- Tools:
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- Visualization with Python
- Google: People + AI Research (PAIR)
- TensorBoard | Google
- TensorFlow Playground | Google
- Facets | Google
- Projector.TensorFlow.org] | Google ...3D visualization of search queries
- Visualize Keras Models with One Line of Code | Lukas & Jeff - Weights & Biases
- Visual DL
- Netron
- Yellowbrick
- CNNVis
- 3D Visualization of a Convolutional Neural Network (CNN)
- ANN Visualizer
- displaCy
- The Five Best Data Visualization Libraries | Lio Fleishman, Sisense
- 5 Python Libraries for Creating Interactive Plots | Melissa Bierly | Mode
- The 9 Best Analytics Tools For Data Visualization Available Today and The 10 Best Data Analytics And BI Platforms And Tools In 2020 | Bernard Marr - Forbes
- Data Visualization for Artificial Intelligence, and Vice Versa | Nicolas Kruchten
- Overview of Model Visualization Architecture and Types | XenonStack
- Understanding ML/DL Models using Interactive Visualization Techniques | Chakri Cherukuri
- A Gentle Introduction to Data Visualization Methods in Python | Jason Brownlee - Machine Learning Mastery
- Virtualitics ... Get more from your data and your team with AI-powered data exploration ... from Descriptive to Prescriptive
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks. Visualization for Deep Learning workshop | Nguyen A, Yosinski J, Clune J, 2016
- Identifying core functional networks and functional modules within artificial neural networks via subsets regression | Velez R, Clune J, 2016
- Feature Engineering and Selection: A Practical Approach for Predictive Models - 4 Exploratory Visualizations | Max Kuhn and Kjell Johnson
Visualization with Python
Shan Carter
