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Page Flows VS PyTorch

Compare Page Flows VS PyTorch and see what are their differences

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Page Flows logo Page Flows

User flow design inspiration for mobile & desktop

PyTorch logo PyTorch

Open source deep learning platform that provides a seamless path from research prototyping to...
  • Page Flows Landing page
    Landing page //
    2019-10-24
  • PyTorch Landing page
    Landing page //
    2023-07-15

Page Flows

Website
pageflows.com
Pricing URL
Official Page Flows Pricing
$ Details
paid $99.0 / Annually
Release Date
2018 February
Edit details

PyTorch

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

Page Flows features and specs

  • Comprehensive Collection
    Page Flows offers a vast library of user flow and design pattern examples from many popular apps and websites, which can be highly valuable for inspiration and learning.
  • High-Quality Content
    The examples are curated and high quality, showcasing best practices in UX and UI design, which can be useful for both beginners and experienced designers.
  • User Experience Focused
    The platform primarily focuses on user flow and UX patterns, providing insights into how to improve usability and user satisfaction.
  • Time-Saver
    By providing a centralized repository of design patterns and flows, it saves time for designers and developers who might otherwise spend hours searching for examples.
  • Updated Regularly
    Page Flows is updated regularly with new content, ensuring users have access to the latest design trends and practices.

Possible disadvantages of Page Flows

  • Paid Subscription
    Accessing the full range of resources and content on Page Flows requires a paid subscription, which might not be affordable for everyone.
  • Niche Focus
    The platform is highly specialized in user flows and design patterns, which might not be useful for everyone, particularly those looking for broader design or development resources.
  • Potential Over-Reliance
    There is a risk that designers might rely too heavily on existing patterns from Page Flows, potentially stifling creativity or leading to a lack of originality in their designs.
  • Learning Curve
    New users might experience a slight learning curve in navigating the platform and making the best use of its resources.

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 Page Flows

Overall verdict

  • Yes, Page Flows is considered a valuable resource.

Why this product is good

  • Page Flows provides a comprehensive collection of user flow examples from popular web and mobile apps, making it an excellent tool for designers and developers seeking inspiration. It helps users understand how different platforms solve design challenges and improve user experience. Additionally, its curated examples and case studies offer insights into best practices and current design trends.

Recommended for

    Page Flows is highly recommended for UX/UI designers, product managers, developers, and anyone involved in app design and improvement. It's especially beneficial for those looking to gather ideas for their own projects or wanting to stay updated with modern design approaches.

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.

Page Flows videos

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PyTorch videos

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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 Page Flows and PyTorch)

Page Flows

PyTorch

Design Tools
100 100%
Design Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Design Inspiration
100 100%
Design Inspiration
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 Page Flows and PyTorch

Page Flows Reviews

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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 seems to be a lot more popular than Page Flows. While we know about 133 links to PyTorch, we've tracked only 10 mentions of Page Flows. 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.

Page Flows mentions (10)

  • Stuck Finding Inspiration? Try These Websites
    Page Flows: This is more of a UX website, but it helps you understand UX better which also helps you understand conversion principles better. Def. Check it’s case studies for yourself. Source: almost 2 years ago
  • Product onboarding - what actually works?
    My favorite place to audit onboarding flows is pageflows. Source: almost 2 years ago
  • UI Design Roadmap 2023
    Step 2: Understand UI design. Https://www.interaction-design.org/literature/topics/ui-design Https://uxplanet.org/what-is-ui-vs-ux-design-and-the-difference-d9113f6612de Visual Understanding Https://mobbin.com/browse/android/apps Https://pageflows.com/ Https://godly.website/ Https://nicelydone.club/. - Source: dev.to / over 2 years ago
  • Breaking Into Legal Tech
    Startup Stash • Tools and resources for entrepreneurs Integrations Directory • Directory of integrations for your no-code product. One Page Love • Find inspiration from one-page websites Do Things That Don’t Scale • Collection of unscalable startup hacks NoCodeList • Software for your projects Page Flows • User design flow inspiration Stackshare • Find software for your projects and business Side Hustle... Source: over 2 years ago
  • Where do you find your inspiration for design? Let's share!
    Page flows is pretty useful. Seeing how other tools solved for similar workflows can definitely spark ideas. Source: over 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 Page Flows and PyTorch, you can also consider the following products

Mobbin - Latest mobile design patterns & elements library

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.

Muz.li - Global directory of product designers

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

UI Movement - The best UI design inspiration, daily

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

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