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TensorFlow VS PyCharm

Compare TensorFlow VS PyCharm and see what are their differences

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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.

PyCharm logo PyCharm

Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...
  • TensorFlow Landing page
    Landing page //
    2023-06-19
  • PyCharm Landing page
    Landing page //
    2023-07-20

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.

PyCharm features and specs

  • Comprehensive IDE
    PyCharm is a full-featured Integrated Development Environment (IDE) that comes with built-in tools for debugging, testing, profiling, and version control, which can significantly enhance productivity.
  • Smart Code Navigation
    PyCharm provides intelligent code navigation features such as code completion, code snippets, and quick jumps to definitions, enabling developers to write code more efficiently.
  • Integrated Tools
    PyCharm integrates with many external tools like Docker, SSH, and terminal, making it easy to manage environments and dependencies directly within the IDE.
  • Built-in Developer Assistance
    PyCharm offers robust developer assistance features such as real-time code analysis, refactoring tools, and coding suggestions, which help maintain code quality.
  • Extensive Plugin Ecosystem
    PyCharm supports a wide range of plugins that can extend its functionality, allowing for customization according to specific development needs or preferences.
  • Cross-Platform Compatibility
    PyCharm is available on multiple platforms including Windows, macOS, and Linux, which ensures that teams working in different environments can use the same toolkit.

Possible disadvantages of PyCharm

  • Resource Intensive
    PyCharm can be quite heavy on system resources, consuming significant memory and CPU, which can slow down the system, especially on machines with lower specifications.
  • High Cost
    PyCharm's Professional Edition is a paid product, which might not be feasible for individual developers or small teams with limited budgets, although a free Community Edition is available.
  • Steep Learning Curve
    Due to its extensive feature set, PyCharm can be overwhelming for beginners, and it may take some time for new users to become proficient with all its functionalities.
  • Occasional Performance Issues
    Some users report occasional performance lags and stability issues, especially when working on large projects or while using certain plugins.
  • Frequent Updates
    While updates are generally a positive feature, PyCharm's frequent updates can sometimes disrupt workflow and necessitate reconfiguring settings or updates to plugins.

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)

PyCharm videos

+ Add

Why Pycharm is the Best Python Editor/IDE!!!

More videos:

  • Review - Best Plugins for PyCharm
  • Tutorial - Pycharm Tutorial #1 - Setup & Basics

Category Popularity

0-100% (relative to TensorFlow and PyCharm)

TensorFlow

PyCharm

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Text Editors
0 0%
Text Editors
100% 100
AI
100 100%
AI
0% 0
IDE
0 0%
IDE
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 TensorFlow and PyCharm

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

PyCharm Reviews

Top 10 Visual Studio Alternatives
PyCharm is a dedicated Python Integrated Development Environment (IDE). It is well-known for offering various vital tools for Python developers. It is securely combined to make a suitable atmosphere for a good level and high productivity Python, website, and data science development process. Moreover, if you are a beginner, the PyCharm can be the one for you.
Source: blog.back4app.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
PyCharm gives you a more professional experience. It isn’t easy to describe, but you’ll understand what I’m talking about after a couple of minutes of usage. The coding assistance is superb, the debugger works like a charm, and the environment management is as easy as it gets.
Source: towardsdatascience.com
The Rise of Microsoft Visual Studio Code
The percentages on this graph are per editor. So we can see, for example, that 97% of engineers using PyCharm program in Python (which makes sense — it's in the name). Eclipse is dominated by Java (94%) and Visual Studio is mostly C# and C++ (88%). I can't really say which way the causality goes, but it seems that both the languages (Java, C#) and the IDEs (Eclipse, Visual...
Source: triplebyte.com
Top 5 Python IDEs For Data Science
Features Just like other IDEs, PyCharm has interesting features such as a code editor, errors highlighting, a powerful debugger with a graphical interface, besides of Git integration, SVN, and Mercurial. You can also customize your IDE, choosing between different themes, color schemes, and key-binding. Additionally, you can expand PyCharm’s features by adding plugins; You...
Source: www.datacamp.com

Social recommendations and mentions

Based on our record, TensorFlow seems to be more popular. 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.

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: almost 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
View more

PyCharm mentions (0)

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

What are some alternatives?

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

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

Microsoft Visual Studio - Microsoft Visual Studio is an integrated development environment (IDE) from Microsoft.

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

IntelliJ IDEA - Capable and Ergonomic IDE for JVM

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

Xcode - Xcode is Apple’s powerful integrated development environment for creating great apps for Mac, iPhone, and iPad. Xcode 4 includes the Xcode IDE, instruments, iOS Simulator, and the latest Mac OS X and iOS SDKs.

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