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Scopus VS TensorFlow

Compare Scopus VS TensorFlow and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Scopus logo Scopus

Scopus is a bibliographic database containing abstracts and citations for academic journal articles.

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.
  • Scopus Landing page
    Landing page //
    2023-03-11
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Scopus features and specs

  • Comprehensive Database
    Scopus provides access to a vast and extensive database of peer-reviewed literature, covering various subjects, which helps researchers find relevant works in their field.
  • Citation Analysis Tools
    Scopus offers robust tools for citation analysis, allowing researchers to track citations over time, analyze trends, and assess the impact of various publications.
  • Indexing of Reputable Journals
    Scopus indexes a wide range of high-quality and reputable journals, ensuring that users have access to credible and authoritative sources.
  • User-friendly Interface
    The Scopus platform is designed with a user-friendly interface, making it easier for researchers to navigate, search, and find the needed information efficiently.
  • Regular Updates
    Scopus is regularly updated with new publications and journals, ensuring that researchers have access to the most current academic literature.

Possible disadvantages of Scopus

  • Costly Subscription
    Access to Scopus typically requires a paid subscription, which can be expensive for individual researchers or small institutions with limited budgets.
  • Limited Access to Full Text
    Scopus primarily serves as an index and does not always provide full-text access to articles, which might require users to access additional resources to retrieve complete documents.
  • Coverage Bias
    There is a tendency for Scopus to index more English-language and Western publications, which can lead to a bias in terms of global research representation.
  • Complexity for Beginners
    The wide range of tools and features available can be overwhelming for new users or those unfamiliar with academic databases, potentially leading to a steep learning curve.
  • Occasional Errors
    As with any large database, there may be occasional errors in citations or metadata, which can affect research accuracy if not cross-referenced with other sources.

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.

Scopus videos

+ Add

Conducting systematic literature review using Scopus: How to refine your search query

More videos:

  • Tutorial - How to write Literature Review : Scopus Paper Series II
  • Review - Best SCOPUS indexed Journals II SCI Journals II Unpaid Journals for Quick Publications

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 Scopus and TensorFlow)

Scopus

TensorFlow

Research Tools
100 100%
Research Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Information Organization
100 100%
Information Organization
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Scopus 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 Scopus and TensorFlow

Scopus Reviews

We have no reviews of Scopus yet.
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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 Franรงois 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 Deepmindโ€™s Acme framework is implemented in TensorFlow. OpenAIโ€™s Baselines model repository is also implemented in TensorFlow, although OpenAIโ€™s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

Based on our record, TensorFlow seems to be more popular. It has been mentiond 8 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.

Scopus mentions (0)

We have not tracked any mentions of Scopus yet. Tracking of Scopus recommendations started around Mar 2021.

TensorFlow mentions (8)

  • Why 70% of Americans See AI as a Wealth Inequality Machine: The Developer's Role in Building Fairer Tech
    The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months ago
  • 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 3 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 4 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 4 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 4 years ago
View more

What are some alternatives?

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

Google Scholar - Google Scholar is a freely accessible web search engine that indexes the full text of scholarly...

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

SemanticScholar - An academic search engine that utilizes artificial intelligence methods to provide highly relevant results and novel tools to filter them with ease.

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

Scinapse - Scinapse is a free, nonprofit, Academic search engine for papers, serviced by Pluto Network.

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

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