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Clever VS TensorFlow

Compare Clever VS TensorFlow and see what are their differences

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Clever logo Clever

syncing between education applications for K-12 schools

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.
  • Clever Landing page
    Landing page //
    2024-10-27
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Clever features and specs

  • Single Sign-On (SSO)
    Clever provides a single sign-on feature that allows students and teachers to log in to multiple educational applications with just one set of credentials, simplifying access and improving security.
  • Data Integration
    The platform seamlessly integrates with various Student Information Systems (SIS) and Learning Management Systems (LMS), allowing for efficient data transfer and synchronization.
  • User-Friendly Interface
    Clever's interface is designed to be intuitive and easy to navigate, which helps reduce the learning curve for both students and educators.
  • Comprehensive App Library
    Clever provides access to a wide array of educational applications, which can be curated and managed by district administrators to meet specific educational needs.
  • Robust Security
    Clever uses industry-standard security protocols and compliance measures to ensure that sensitive student data is protected.
  • Cost Efficiency
    By centralizing access and data management, Clever can help educational institutions reduce costs associated with managing multiple platforms and licenses.

Possible disadvantages of Clever

  • Vendor Lock-In
    Relying heavily on Clever for integration and access management can lead to vendor lock-in, making it difficult for schools to switch to alternative solutions.
  • Dependence on Internet
    Clever's functionality is highly dependent on a stable internet connection, which can be an issue in areas with poor connectivity.
  • Initial Setup Complexity
    Setting up Clever to work seamlessly with all integrated systems and applications can be complex and time-consuming, requiring technical expertise.
  • Limited Customization
    While Clever offers many features, the ability to customize the platform to suit specific district or school needs may be limited compared to other solutions.
  • Privacy Concerns
    Despite robust security measures, the centralized nature of Clever's data management can raise privacy concerns among parents and educators.
  • Inconsistent App Performance
    Some users may experience inconsistent performance across different educational apps within Clever, which can disrupt the learning process.

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.

Analysis of Clever

Overall verdict

  • Clever is considered a good tool for schools and educational institutions looking to improve their digital learning environment. Its ease of use, broad integration with educational applications, and secure access management make it a valuable asset for modern classrooms.

Why this product is good

  • Clever is a widely adopted educational platform that simplifies login processes and streamlines access to a variety of educational applications for K-12 students, teachers, and administrators. It aims to enhance the learning experience by providing a secure and efficient digital hub, allowing users to access multiple learning tools with a single set of login credentials.

Recommended for

  • K-12 schools looking to integrate digital learning tools seamlessly.
  • Teachers who want a centralized platform to access educational applications.
  • Administrators who need simplified management of student and staff access to digital resources.
  • Parents and students seeking an easy-to-use login system for educational apps.

Clever videos

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TensorFlow videos

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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 Clever and TensorFlow)

Clever

TensorFlow

Education
100 100%
Education
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Education
100 100%
Online Education
0% 0
AI
0 0%
AI
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 Clever and TensorFlow

Clever Reviews

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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, TensorFlow should be more popular than Clever. It has been mentiond 7 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.

Clever mentions (2)

  • I can't access my schools website using the Tor network.
    I tried it on the Firefox and Tor Browser on Whonix, same error pops up because it's using the Tor network? Is there any way I can bypass this error so I can visit my schools website, or another way to use the site anonymously? Site is Clever. Source: over 2 years ago
  • Learned helplessness
    Mine also don't know what bookmarks are. So to get into Schoology, they type clever.com into the search bar - not the address bar - then log into it, then click the student page, then find Schoology, then click it. And the wifi in my part of the building sucks, so it takes them 5 minutes. Source: over 2 years ago

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 / over 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: about 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 Clever and TensorFlow, you can also consider the following products

PowerSchool - PowerSchool provides a K-12 education technology platform for operations, classroom, student growth, and family engagement.

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

Teachable - Create and sell beautiful online courses with the platform used by the best online entrepreneurs to sell $100m+ to over 4 million students worldwide.

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

Claroline - Claroline is a collaborative eLearning and eWorking platform.

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

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