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Keywords Everywhere VS PyTorch

Compare Keywords Everywhere VS PyTorch and see what are their differences

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Keywords Everywhere logo Keywords Everywhere

Free browser add-on for keyword volume, CPC & competition

PyTorch logo PyTorch

Open source deep learning platform that provides a seamless path from research prototyping to...
  • Keywords Everywhere Landing page
    Landing page //
    2023-09-19
  • PyTorch Landing page
    Landing page //
    2023-07-15

Keywords Everywhere features and specs

  • Comprehensive Metrics
    Keywords Everywhere provides detailed metrics such as search volume, CPC, and competition data, helping users make informed decisions for SEO and PPC strategies.
  • Ease of Use
    The browser extension integrates seamlessly with essential tools like Google Search, YouTube, and Google Analytics, making it convenient to access keyword data directly from these platforms.
  • Affordability
    Offers a pay-as-you-go pricing model, which can be more cost-effective for small businesses and individual users compared to subscription-based services.
  • Data Across Platforms
    Provides keyword data for multiple platforms including Google, YouTube, Amazon, and more, which is valuable for diverse digital marketing strategies.
  • Time Saver
    By displaying keyword metrics directly in search engine results and other tools, it significantly reduces the time needed to gather and analyze keyword data.

Possible disadvantages of Keywords Everywhere

  • Limited Free Version
    The free version offers very limited features, driving users to purchase credits for more comprehensive data.
  • Dependency on Browser Extension
    Requires a browser extension to function, which may not be suitable for all users or devices and could raise privacy/security concerns.
  • Accuracy Variability
    As with many keyword tools, the accuracy of the data can occasionally be inconsistent, which may affect strategic decisions.
  • Limited Advanced Features
    While great for basic keyword research, it lacks some of the advanced features offered by more robust SEO tools, such as detailed competitive analysis or site audits.
  • Potential for Data Overload
    The abundance of data displayed can sometimes be overwhelming, particularly for beginners who may struggle to interpret and utilize it effectively.

PyTorch features and specs

  • Dynamic Computation Graph
    PyTorch uses a dynamic computation graph, which allows for interactive and flexible model building. This is particularly beneficial for researchers who need to modify the network architecture on-the-fly.
  • Pythonic Nature
    PyTorch is designed to be deeply integrated with Python, making it very intuitive for Python developers. The framework feels more 'native' to Python, which improves the ease of learning and use.
  • Strong Community Support
    PyTorch has a large, active, and growing community. This means abundant resources such as tutorials, forums, and third-party tools are available to help developers solve problems and share solutions.
  • Flexibility and Control
    PyTorch offers granular control over computations and provides extensive debugging capabilities. This level of control is beneficial for tasks that require precise tuning and custom implementations.
  • Support for GPU Acceleration
    PyTorch offers seamless integration with GPU hardware, which significantly accelerates the computation process. This makes it highly efficient for deep learning tasks.
  • Rich Ecosystem
    PyTorch has a rich ecosystem including libraries like torchvision, torchaudio, and torchtext, which are specialized for different data types and can significantly shorten development times.

Possible disadvantages of PyTorch

  • Limited Production Deployment Tools
    PyTorch is primarily designed for research rather than production. While deployment tools like TorchServe exist, they are not as mature or integrated as solutions offered by other frameworks like TensorFlow.
  • Lesser Adoption in Industry
    While PyTorch is popular among researchers, it has historically seen less adoption in industry compared to TensorFlow, which means there might be fewer resources for large-scale production deployments.
  • Inconsistent API Changes
    As PyTorch continues to evolve rapidly, occasionally there are breaking changes or inconsistent API updates. This can create maintenance challenges for existing codebases.
  • Steeper Learning Curve for Beginners
    Despite its Pythonic design, PyTorch's focus on flexibility and control can make it slightly harder for beginners to get started compared to some other high-level libraries and frameworks.
  • Less Mature Documentation
    Although the documentation is improving, it has been historically less comprehensive and mature compared to other frameworks like TensorFlow, which can make it difficult to find detailed, clear information.

Analysis of PyTorch

Overall verdict

  • Yes, PyTorch is considered a good deep learning framework.

Why this product is good

  • Ease of Use: PyTorch has an intuitive interface that makes it easier to learn and use, especially for beginners.
  • Dynamic Computation Graphs: PyTorch employs dynamic computation graphs, which provide more flexibility in building and modifying models on the fly.
  • Strong Community and Support: PyTorch has a large and active community, offering extensive resources, forums, and tutorials.
  • Research Adoption: PyTorch is widely adopted in the research community, making state-of-the-art models and techniques readily available.
  • Integration: PyTorch integrates well with other libraries and tools in the Python ecosystem, providing robust support for various applications.

Recommended for

  • Researchers and Academics: Ideal for those who need a flexible and dynamic tool for experimenting with new models and techniques.
  • Industry Practitioners: Suitable for developers and data scientists working on production-level machine learning solutions.
  • Educators and Learners: Great for educational purposes due to its easy-to-understand syntax and comprehensive documentation.

Keywords Everywhere videos

+ Add

How to use Keywords Everywhere - SEO keyword research tool

More videos:

  • Review - KEYWORDS EVERYWHERE is now a PAID TOOL - Here's What To Do - Keywords Everywhere Alternative
  • Tutorial - Keywords Everywhere | A Tutorial + Advice on Keywords for YouTube
  • Review - Keywords Everywhere Review: Better Alternative to Google Keyword Planner
  • Review - Keywords Everywhere Review | Best Keyword Search Volume Chrome Extension! 🚀
  • Review - Keywords Everywhere Review 2021 | Keyword research Tool

PyTorch videos

+ Add

PyTorch in 5 Minutes

More videos:

  • Review - Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai | AI Podcast Clips
  • Review - PyTorch at Tesla - Andrej Karpathy, Tesla

Category Popularity

0-100% (relative to Keywords Everywhere and PyTorch)

Keywords Everywhere

PyTorch

SEO Tools
100 100%
SEO Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
SEO
100 100%
SEO
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Keywords Everywhere and PyTorch

Keywords Everywhere Reviews

112 Best Chrome Extensions You Should Try (2021 List)
Keywords Everywhere is an alternative to Ubersuggest, a freemium keyword research tool. It shows the search query data on more than 15 websites. For free users, it shows a trend chart, long-tail keywords, and keywords from ‘people also search for’. But, paid users can see monthly search volume, CPC, competition, and trend data. Although solely for keyword research, you do...
Source: www.einsstark.tech
9 Free Keyword Research Tools (That CRUSH Google Keyword Planner)
Keywords Everywhere is a free add-on for Chrome (or Firefox). It adds search volume, CPC & competition data to all your favourite websites.
Source: ahrefs.com
73 Best SEO tools 2021 – The Most Epic List You Shouldn’t Miss
While most use this tool strictly for Paid ads, Keywords Everywhere is very useful to help you discover long-tail keywords related to the ones you are searching for on Google.
Source: blog.linkody.com

PyTorch Reviews

10 Python Libraries for Computer Vision
Similar to TensorFlow and Keras, PyTorch and torchvision offer powerful tools for computer vision tasks. PyTorch’s dynamic computation graph and torchvision’s datasets and pre-trained models make it easy to implement tasks such as image classification, object detection, and style transfer.
Source: clouddevs.com
25 Python Frameworks to Master
Along with TensorFlow, PyTorch (developed by Facebook’s AI research group) is one of the most used tools for building deep learning models. It can be used for a variety of tasks such as computer vision, natural language processing, and generative models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
PyTorch is another open-source machine learning framework that is widely used in academia and industry. PyTorch provides excellent support for building deep learning models, and it has several pre-trained models for computer vision tasks, making it the ideal tool for several computer vision applications. PyTorch offers a user-friendly interface that makes it easier for...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
When we compare HuggingFace model availability for PyTorch vs TensorFlow, the results are staggering. Below we see a chart of the total number of models available on HuggingFace that are either PyTorch or TensorFlow exclusive, or available for both frameworks. As we can see, the number of models available for use exclusively in PyTorch absolutely blows the competition out of...
Source: www.assemblyai.com
15 data science tools to consider using in 2021
First released publicly in 2017, PyTorch uses arraylike tensors to encode model inputs, outputs and parameters. Its tensors are similar to the multidimensional arrays supported by NumPy, another Python library for scientific computing, but PyTorch adds built-in support for running models on GPUs. NumPy arrays can be converted into tensors for processing in PyTorch, and vice...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, PyTorch should be more popular than Keywords Everywhere. It has been mentiond 133 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.

Keywords Everywhere mentions (16)

  • SEO 101 for Software Developers
    To find keywords I use the tool Keywords Everywhere. It gives you information on how many people search for a particular keyword a month, how difficult it will be to rank for, as well ideas for additional keywords. - Source: dev.to / over 1 year ago
  • How to Manage Your Time as a Software Developer ⌛️
    For example, I do a lot of keyword research for my blog posts and YouTube videos. This generally consists of searching for keywords on Google and then copying the numbers that I get from Keywords Everywhere into a spreadsheet. - Source: dev.to / about 2 years ago
  • My Guide To Shopify Store Keyword Research
    You may be thinking to yourself well that's it right? I know what works and what doesn't, well not exactly because you don't just want to copy everything your competition does or you'll be competing with them all the time and that's a losing battle for most small stores. So step 2 is I cross reference it with another tool called keywords everywhere. As I mentioned this tool can be similar to Ahrefs as you can scan... Source: about 2 years ago
  • GMB Stats?
    Keywords everywhere again, not sure if it's match for you. Source: about 2 years ago
  • Keyword research
    Step 2: keywordseverywhere.com ($10 for 100K SV check - it's a chrome extension), run your list through this and get all SV. Source: about 2 years ago
View more

PyTorch mentions (133)

  • Grasping Computer Vision Fundamentals Using Python
    To aspiring innovators: Dive into open-source frameworks like OpenCV or PyTorch, experiment with custom object detection models, or contribute to projects tackling bias mitigation in training datasets. Computer vision isn’t just a tool, it’s a bridge between the physical and digital worlds, inviting collaborative solutions to global challenges. The next frontier? Systems that don’t just interpret visuals, but... - Source: dev.to / about 1 month ago
  • Top Programming Languages for AI Development in 2025
    With the quick emergence of new frameworks, libraries, and tools, the area of artificial intelligence is always changing. Programming language selection. We're not only discussing current trends; we're also anticipating what AI will require in 2025 and beyond. - Source: dev.to / about 2 months ago
  • Fine-tuning LLMs locally: A step-by-step guide
    Next, we define a training loop that uses our prepared data and optimizes the weights of the model. Here's an example using PyTorch:. - Source: dev.to / 2 months ago
  • 10 Must-Have AI Tools to Supercharge Your Software Development
    8. TensorFlow and PyTorch: These frameworks support AI and machine learning integrations, allowing developers to build and deploy intelligent models and workflows. TensorFlow is widely used for deep learning applications, offering pre-trained models and extensive documentation. PyTorch provides flexibility and ease of use, making it ideal for research and experimentation. Both frameworks support neural network... - Source: dev.to / 4 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    Frameworks like TensorFlow and PyTorch can help you build and train models for various tasks, such as risk scoring, anomaly detection, and pattern recognition. - Source: dev.to / 4 months ago
View more

What are some alternatives?

When comparing Keywords Everywhere and PyTorch, you can also consider the following products

KeywordTool.io - KeywordTool.io is the best FREE alternative to Google Keyword Planner and Ubersuggest. It uses Google's autocomplete feature to get over 750+ long-tail keywords for any given query.

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.

Moz - Backed by industry-leading data and the largest community of SEOs on the planet, Moz builds tools that make inbound marketing easy.

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

Ahrefs - Ahrefs is a toolset for SEO and marketing. We have tools for backlink research, organic traffic research, keyword research, content marketing & more. Give Ahrefs a try!

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

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