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TensorFlow VS Tableau

Compare TensorFlow VS Tableau and see what are their differences

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TensorFlow logo TensorFlow

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Tableau logo Tableau

Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
  • TensorFlow Landing page
    Landing page //
    2023-06-19
  • Tableau Landing page
    Landing page //
    2023-10-18

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Tableau features and specs

  • User-Friendly Interface
    Tableau offers an intuitive drag-and-drop interface that allows users to create visualizations and dashboards easily, even without extensive technical knowledge.
  • Data Connectivity
    Tableau supports a wide range of data sources including databases, spreadsheets, cloud services, and more, allowing for flexible data integration.
  • Advanced Analytics
    Advanced analytical capabilities, including real-time analytics, trend analysis, and predictive analytics, help users gain deeper insights from their data.
  • Community and Support
    A large, active user community provides a wealth of resources including forums, tutorials, and user groups for support and knowledge sharing.
  • Visualization Quality
    Tableau offers high-quality visualizations with customizable options that make it easier to create compelling reports and dashboards.

Possible disadvantages of Tableau

  • Cost
    Tableau can be expensive, especially for small businesses or individual users, with its various licensing and subscription fees.
  • Performance Issues
    For very large datasets or complex calculations, Tableau can experience performance slowdowns, affecting the efficiency and user experience.
  • Steep Learning Curve for Advanced Features
    While basic features are easy to use, mastering advanced functionalities can require a significant learning curve and technical expertise.
  • Customization Limitations
    Although Tableau is highly customizable, some users find it lacks flexibility when it comes to very specific or unique customization requirements.
  • Export Limitations
    Exporting visualizations and dashboards to formats like PDF or PowerPoint can sometimes be restrictive, limiting the ways reports are shared.

TensorFlow videos

+ Add

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

Tableau videos

+ Add

Power BI vs Tableau 🔥 5 Factors to Choose a Winner

More videos:

  • Review - What is Tableau Desktop? | A Tableau Desktop Overview
  • Demo - Tableau Software Demo

Category Popularity

0-100% (relative to TensorFlow and Tableau)

TensorFlow

Tableau

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
AI
100 100%
AI
0% 0
Data Visualization
0 0%
Data Visualization
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare TensorFlow and Tableau

TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
Source: www.labellerr.com
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

Tableau Reviews

Explore 7 Tableau Alternatives for Data Visualization and Analysis
Welcome to our complete reference, Tableau Alternatives for Data Visualization and Analysis. In this fast-changing digital age, data visualization and analysis have become critical for making informed decisions and strategies. Tableau is a well-known product that has had a considerable impact in this sector. Its user-friendly interface and powerful capabilities have made it...
Source: www.draxlr.com
Explore 6 Metabase Alternatives for Data Visualization and Analysis
To find the best Metabase alternative for your business, start by listing your specific requirements, such as customer support, data integrations, visualization options, user access controls, and budget. Compare these needs with the features of other BI tools like Draxlr, Tableau, Power BI, Looker, or Holistics. Once you've identified a few suitable options, take advantage...
Source: www.draxlr.com
5 best Looker alternatives
Tableau: Tableau is the earliest BI tools built to solve data problems, which means it has a lot of community support for all your queries and can lack what the new-age tools have and are building.
Source: www.draxlr.com
10 Best Alternatives to Looker in 2024
Tableau: Renowned for its powerful visualization capabilities, Tableau enables users to create highly intuitive and interactive dashboards. Favored for its user-friendly interface, Tableau effectively handles large datasets, making it a top choice for data visualization.
Source: www.sigmacomputing.com
6 Best Looker alternatives
Pricing: Looker is typically more expensive than Tableau – but if you’re a growing company with increasing user numbers, Tableau can also get pricey.
Source: trevor.io

Social recommendations and mentions

Tableau might be a bit more popular than TensorFlow. We know about 8 links to it since March 2021 and only 7 links to TensorFlow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

TensorFlow mentions (7)

  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / about 2 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: almost 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
View more

Tableau mentions (8)

  • Tableau Certified Data Analyst Exam Readiness
    Hey everyone, I'm interested in taking the Tableau Certified Data Analyst Exam Readiness course through tableau.com to prepare and get Tableau certified. I had some questions about the course, such as are the videos pre recorded or in person, do you have access to the material once the 90 days expire, and I was also wondering if anyone had input/advice for this course. Thanks! Source: almost 2 years ago
  • Where to publish knowledge sharing on Tableau reverse engineering and data dictionary generation?
    Could anyone recommend what media I should approach to publish my work (internet or print). I could try the Tableau forum in tableau.com but it's not very active + Tableau may be unappreciative as my work overlaps with their (pricey) data management solution. Plus it needs to be some high visibility / reputable media to count for my career development. Any recommendations welcome thanks!!! Source: over 2 years ago
  • I have huge loads of data in Redshift. How can I make this available to end-users after performing few procs and queries? It should be available online.
    Tableau public: tableau.com. Big player but your data will be made public and not really user-friendly data model. Source: about 3 years ago
  • What tips do you have on evaluating various BI tools for business needs? What are the essential criteria's you would include when evaluating different tools? The goal is to have an unbiased, objective approach.
    For example, we have a project to compare Tableau, Power BI, and InetSoft. The need for strong pagination-based email delivery eliminated Tableau. AWS's Linux instance is the targeted platform which makes Power BI less than ideal. Source: about 3 years ago
  • Anyone go into Data Analytics after this program?
    I just started learning Tableau because our dept is transitioning into Tableau from Power BI. Since I already have years of experience with Power BI I just went over their tutorials from tableau.com and got onboarded pretty quick. I'm still learning it but I'm at least able to build out reports and get things done. Its not too difficult to pickup one BI tool when you have experience with another. Source: about 3 years ago
View more

What are some alternatives?

When comparing TensorFlow and Tableau, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

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