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HackerRank VS PyTorch

Compare HackerRank VS PyTorch and see what are their differences

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HackerRank logo HackerRank

HackerRank is a platform that allows companies to conduct interviews remotely to hire developers and for technical assessment purposes.

PyTorch logo PyTorch

Open source deep learning platform that provides a seamless path from research prototyping to...
  • HackerRank Landing page
    Landing page //
    2023-07-23
  • PyTorch Landing page
    Landing page //
    2023-07-15

HackerRank features and specs

  • Skill Assessment
    HackerRank provides a structured way to assess coding skills through a wide range of programming challenges and problems.
  • Wide Range of Languages
    Supports numerous programming languages, making it versatile for users with different preferences and expertise.
  • Interview Preparation
    Offers various interview preparation kits and company-specific challenges to help candidates prepare for job interviews.
  • Community and Collaboration
    A community of coders where users can discuss problems, share solutions, and collaborate on coding projects.
  • Company Recruitments
    Many companies use HackerRank for recruitment, and performing well on the platform can lead to job opportunities.
  • Leaderboard and Gamification
    Features like leaderboards and gamification elements motivate users to improve their rankings and skills continuously.
  • Educational Resources
    Provides tutorials and explanations that help users understand algorithms and data structures better.

Possible disadvantages of HackerRank

  • Steep Learning Curve
    Beginners may find some problems too challenging, which can be discouraging if they lack foundational knowledge.
  • Potential Focus on Competitive Programming
    The platform may emphasize competitive programming skills, which are not always directly applicable to all real-world software development scenarios.
  • Quality Variance in Problems
    The quality and difficulty of problems can vary, which may affect the consistency of the learning experience.
  • Limited Real-World Project Experience
    The focus on algorithms and coding challenges means there's less emphasis on full-scale project development experience.
  • Limited Feedback
    Automated grading provides limited feedback, which may not be enough for users to understand their mistakes fully.
  • Subscription Costs
    Access to some premium content and features requires a subscription, which may not be affordable for all users.
  • Network Dependency
    Requires a good internet connection to participate in coding challenges and access resources, which may be a limitation for some users.

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.

HackerRank videos

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Is HackerRank A Good Idea?

More videos:

  • Review - LeetCode vs HackerRank
  • Review - Difference between HackerRank, LeetCode, topcoder and Codeforces

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 HackerRank and PyTorch)

HackerRank

PyTorch

Hiring And Recruitment
100 100%
Hiring And Recruitment
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Learning
100 100%
Online Learning
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 HackerRank and PyTorch

HackerRank Reviews

LeetCode Alternatives: Top platforms for coding practice
What are LeetCode and LeetCode alternatives good for?LeetCode💡Interested in leveling up your career? Apply to the Formation Fellowship today!ApplyHackerRankCodeSignalAlgoExpertCodewarsGeeksforGeeksEdabitExercismTopCoderShould you use LeetCode for advanced interview prep?Get holistic interview prep with Formation
Source: formation.dev
Top 10 Developer Communities You Should Explore
HackerRank’s challenges cover a wide range of topics and difficulty levels, allowing developers to enhance their problem-solving skills and learn new algorithms and data structures. The competitive nature of HackerRank challenges adds a fun element to the learning process. Developers can track their progress, compete with others, and participate in company-sponsored coding...
Source: www.qodo.ai
Discover the Top Leetcode Alternatives
HackerRank offers a wide array of challenges across various domains such as algorithms, mathematics, SQL, and functional programming. Its interface is user-friendly, and the platform provides detailed feedback on submissions, which is ideal for beginners and experienced coders alike.
Source: codenquest.com
Best Alternatives to LeetCode For Data Science
HackerRank is another valuable alternative to LeetCode. They're not very "niche" but I had to include them on this list because they're a great resource for data science practice. On HackerRank, you can learn and test your competitive programming skills. If you have basic knowledge of Python and SQL and you're looking to sharpen your skills for an interview, then this...
Source: www.stratascratch.com
15 Best LeetCode Alternatives 2023
HackerRank is a platform that matches developers with companies. The platform has two options. The first one is for companies looking to hire developers. The second option is for job seekers looking to improve their coding skills, prepare for interviews, and get hired.
Source: rigorousthemes.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 HackerRank. It has been mentiond 132 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.

HackerRank mentions (66)

  • Pick up new languages faster this way!
    Firstly, solve some common data structure problems with it. Implement some data structures like arrays, linked lists, stacks, queues, etc. You can check common problems on LeetCode, Hackerank or some other resources. - Source: dev.to / about 1 year ago
  • Offline alternative of hackerrank.com to practice coding offline
    I don't have a consecutive internet connection and I can't keep up learning process so I started practicing in hackerrank.com I have started some challenges in python and c++ there. Thus I have no internet connection so I cannot practice if anyone know any alternative that works like Working: Gives a challange User sumbits code and it test into testcases. Source: over 1 year ago
  • 6 Key Tips for Beginners Learning JavaScript
    An effective way to improve your JavaScript skills is working through coding challenges and exercises. Sites like ReviewNPrep, FreeCodeCamp, and HackerRank have tons of challenges that allow you to practice JavaScript concepts by building mini-projects and solving problems. These hands-on challenges force you to apply what you learn. Source: over 1 year ago
  • Help needed for selecting Colleges.
    I'm 18M Indian. Growing up I've always been a daydreamer, if you may. Since 8th grade - I'm fascinated by programming. And I'm good at it too. But I'm not cocky too. I wouldn't say I'm at an advanced level, but I can most probably solve any problem - in time - with my skills. I also keep my skills brushed by solving problems on Hacker Rank (every day or alternate days) and try my best to contribute on... Source: over 1 year ago
  • Which is best, i didn't have clue what is c language, programing is this is the best video on YouTube , which should i chose or tell me in comments for a better course
    You can try Jenny's lectures. https://www.youtube.com/playlist?list=PLdo5W4Nhv31a8UcMN9-35ghv8qyFWD9_S if you like classroom style teaching with whiteboard. For programming ,apart from tutorials the thing that helps best is practice , If you want to practice then I recommend hackerrank.com to test your understanding of programming concepts. Source: about 2 years ago
View more

PyTorch mentions (132)

  • 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 / 12 days 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 / about 1 month 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 / 3 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 / 3 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
View more

What are some alternatives?

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

LeetCode - Practice and level up your development skills and prepare for technical interviews.

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.

Codility - Codility provides a SaaS platform with advanced validation, security and protection features to evaluate the skills of software engineers.

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

CodeSignal - CodeSignal is the leading assessment platform for technical hiring.

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

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