this ppt contains tableau in deep learning techniques

to learn about the tableau in deep learning technique

1. TABLEAU (DEEP LEARNING TECHNIQUES) B JYOTSNA 24CSER009 MSC AI

2. INTRODUCTION Tableau is a data visualization tool essential to data analysts of all experience levels In deep learning (DL), it plays a vital role in helping to visualize complex data and model outputs. Tableau can be used to explore data before training models, visualize performance metrics like accuracy and loss curves, and compare different models or hyperparameters. Additionally, it aids in interpreting the results of deep learning models, such as understanding feature importance and detecting patterns in predictions. By connecting to various data sources, Tableau helps teams make informed decisions, track model performance, and monitor ongoing predictions.

3. Common Tableau uses Tableau allows users to translate raw data into highly visual dashboards and analyses, that are ideal for explaining data trends to non-technical stakeholders. The most common uses include: Creating maps, bar charts, line charts, scatter plots, and more. Providing contextual data information as needed through Tooltips, a brief analysis shown when you hover over your analysis. Linking sheets so that users can dig deeper into data analysis.

4. Creating your first Tableau visualization You can create a visualization in five steps: STEP 1: Select your data in the Data Source tab.

5. STEP 2 : Open a blank sheet.

6. STEP 3 : Select the fields to use. Tip: Hold the Control key (Command key on Macs) to select multiple fields

7. STEP 4 : Click the “Show me” button to select a visualization type. Available vizzes are highlighted.

8. STEP 5 : Select the type of visualization and watch Tableau instantly create it. To adjust the viz, drag and drop fields to or from the presentation.

9. You can expand your vizzes in a variety of ways: Dashboards: Avoid a cluttered dashboard by using Tooltips to highlight displayed data and add context by showing other values or vizzes within the Tooltip. Dashboards can be used to show multiple vizzes together on a single page. To do so, create a blank dashboard, then drag and drop as many visualizations as desired on to it. Filter: Use filters in a variety of ways to hone in on specific data. You can filter data to focus on a particular category. Or, use one viz to act as a filter for a second viz. For example, in a viz about a retail chain, select a store location, then explore a separate viz for that particular store. Story: Tableau offers a feature called story, which is a sequence of visualizations that help you present a cohesive data analysis. This allows you to stage a story exactly how you’d like it without having to readjust filters each time the related dashboard is accessed. Comparisons: Make data comparisons by using visual analytics like trend lines or averages.

10. CONTINUE Formulas: Build additional calculations using formulas. While Tableau formulas are derived from standard Excel formulas, more advanced formulas are available. For example, a time intelligence formula compares present values against values from a week ago. Maps: Analyses that include geographic locations can be displayed in maps with values represented as symbols, colors, or heat. Explain Data: In version 2019.3, Tableau added a new feature called Explain Data, which builds artificial intelligence technology into the analysis, so that analysts can understand the “Why?” behind data.

11. EG: Tableau R etail C ompany dashboard Scenario: A retail company wants to analyze its sales performance across different regions. How Tableau Helps: 1. Data Connection: Connects to the company's sales database (Excel, SQL, or cloud storage). 2. Data Processing: Cleans and organizes sales data (e.g., filtering by product, region, or time). 3. Visualization: Creates interactive dashboards with: A bar chart showing sales by region. A line graph tracking sales trends over months. 4. I nsights & Decisions: Identifies which regions need marketing improvements. Helps predict future sales trends. Enables management to make data-driven decisions.

12. REAL-WORLD EXAMPLE: Amazon uses Tableau to analyze customer purchase patterns, and improve sales strategies.

13. SOME OF EXAMPLE OF TABLEAU Tableau executive dashboard for the hospitality industry Tableau sales dashboard Tableau marketing dashboard Tableau call center dashboard Tableau HR dashboard Tableau project management dashboard Tableau financial dashboard Tableau e-commerce dashboard

14. CONCLUSION Tableau enhances deep learning workflows by enabling data blending, interactive dashboards, and real-time updates. It supports visualizing training progress, datasets, and model predictions, helping to interpret results effectively. Tableau plays a valuable role in simplifying the analysis of deep learning models. With future advancements like integration with AI frameworks and explainable AI tools, it has the potential to become an even more powerful tool for interpreting complex models and driving data-driven decision-making.

15. REFERENCE LINK https://blog.udemy.com/what-is-tableau-understanding-an-analysts-most-important-tool/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_DSA_GammaCatchall_NonP_la.EN_cc.India&campaigntype=Search&portfolio=India&language=EN&product=Course&test=&audience=DSA&topic=&priority=Gamma&utm_content=deal4584&utm_term=_._ag_166578677881_._ad_700948726520_._kw__._de_m_._dm__._pl__._ti_dsa-1456167871416_._li_9148884_._pd__._&matchtype=&gad_source=1&gclid=Cj0KCQiAkoe9BhDYARIsAH85cDN4aP3-2nlEAmDTKFM_qXIXQGw0mDSJQ_6rNpmtlXh-cDspCeufYiYaAhj0EALw_wcB https://blog.coupler.io/tableau-dashboard-examples/

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