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Rows VS TensorFlow

Compare Rows VS TensorFlow and see what are their differences

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Rows logo Rows

The spreadsheet where teams work faster

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.
  • Rows Landing page
    Landing page //
    2023-02-23

Slick design. Built-in integrations. Revolutionary sharing. Rows reinvented spreadsheets so teams do more, crazy fast.

  • TensorFlow Landing page
    Landing page //
    2023-06-19

Rows

Website
rows.com
Pricing URL
Official Rows Pricing
$ Details
-
Release Date
2016 January
Startup details
Country
Germany
State
Berlin
City
Berlin
Founder(s)
Humberto Ayres Pereira
Employees
10 - 19
Edit details

TensorFlow

Website
tensorflow.org
Pricing URL
-
$ Details
Release Date
-
Edit details

Rows features and specs

  • User-Friendly Interface
    Rows provides an intuitive and easy-to-use spreadsheet interface that is accessible for users of all skill levels, from beginners to advanced.
  • Integration Capabilities
    Supports a variety of integrations with other software services and APIs, allowing for seamless data import and export.
  • Real-Time Collaboration
    Allows multiple users to work on the same spreadsheet simultaneously, enhancing team productivity and ensuring everyone has the latest information.
  • Customization and Automation
    Offers powerful automation features and the ability to write custom scripts, which can save time and reduce manual errors.
  • Template Library
    Provides a rich library of pre-designed templates that can help users quickly get started on common business tasks.

Possible disadvantages of Rows

  • Learning Curve
    While user-friendly, more advanced features and scripting capabilities may require a significant learning curve for new users.
  • Limited Offline Functionality
    Primarily a cloud-based tool, which means it relies heavily on internet connection and offers limited offline functionality.
  • Pricing
    The cost of premium features or larger scale deployments can be high, which may not be affordable for small businesses or individual users.
  • Dependency on Integrations
    Heavily reliant on third-party integrations, which means any issues or changes in connected services can impact Rows' functionality.
  • Security Concerns
    As with any cloud-based service, there may be concerns about data security and privacy, especially for sensitive or confidential information.

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.

Rows videos

+ Add

Welcome to Rows

More videos:

  • Review - The Truth about Barbell Rows (AVOID MISTAKES!)
  • Review - 9/21/21 bentover rows review

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)

Category Popularity

0-100% (relative to Rows and TensorFlow)

Rows

TensorFlow

Spreadsheets
100 100%
Spreadsheets
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Productivity
100 100%
Productivity
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Rows and TensorFlow. For example, how are they different and which one is better?
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Reviews

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

Rows Reviews

The best no-code tools for sales teams
You can bring your data to life. With Rows, you can jazz up your spreadsheets with slick charts, images, audio and even interactive features such as buttons and checkboxes. What’s more, you can share your spreadsheets with colleagues and clients in the form of interactive dashboards and websites.
Source: www.nocode.tech

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

Social recommendations and mentions

Based on our record, Rows should be more popular than TensorFlow. It has been mentiond 24 times since March 2021. 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.

Rows mentions (24)

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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
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What are some alternatives?

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

Airtable - Airtable works like a spreadsheet but gives you the power of a database to organize anything. Sign up for free.

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

NocoDB - The Open Source Airtable alternative

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

Baserow - Open source no-code database and Airtable alternative. Create your own online database without technical experience. Performant with high volumes of data, can be self hosted and supports plugins

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

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