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Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka

** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learni... ** <br>This Edureka PPT on "Keras vs TensorFlow vs PyTorch" will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this PPT: <br>Introduction to keras, Tensorflow, Pytorch <br>Parameters of Comparison <br>Level of API <br>Speed <br>Architecture <br>Ease of Code <br>Debugging <br>Community Support <br>Datasets <br>Popularity <br>Suitable use cases <br><br>Follow us to never miss an update in the future. <br><br>Instagram: https://www.instagram.com/edureka_learning/ <br>Facebook: https://www.facebook.com/edurekaIN/ <br>Twitter: https://twitter.com/edurekain <br>LinkedIn: https://www.linkedin.com/company/edureka

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Keras vs Tensorflow vs PyTorch | Deep Learning Frameworks Comparison | Edureka

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Presentation Transcript

  1. ▪ An open source neural network library ▪ An open source library for dataflow programming ▪ An open source machine learning library ▪ Runs on top of Tensorflow ▪ Used for Machine learning applications ▪ Developed by Facebook’s AI

  2. Community Ease of Code Popularity Speed Debugging Dataset Level of API Architecture

  3. Community Ease of Code Popularity Speed Debugging Dataset Level of API Architecture

  4. Level of API Low level API High level API Provides high level & low level API

  5. Speed Community Ease of Code Popularity Debugging Dataset Level of API Architecture

  6. Speed Slower as compared to Pytorch Used for high Performance Equivalent to the speed of TensorFlow

  7. Community Ease of Code Popularity Speed Debugging Dataset Level of API Architecture

  8. Architecture Architecture is simpler than Pytorch Not that easy to use Complex architecture

  9. Ease of Code Community Popularity Speed Debugging Dataset Level of API Architecture

  10. Ease of Code Reduced size of Model with high accuracy More number of lines in code Single line code

  11. Community Ease of Code Popularity Speed Dataset Level of API Architecture Debugging

  12. Debugging Less Frequent need to debug simple networks Debugging is difficult Better Debugging capabilities

  13. Community Ease of Code Popularity Speed Debugging Dataset Level of API Architecture

  14. Community Smaller community support Backed by a large community of tech companies Stronger community support

  15. Community Ease of Code Popularity Speed Debugging Level of API Architecture Dataset

  16. Dataset Used for high performance models Used for small dataset Used for large datasets

  17. Popularity Community Ease of Code Speed Debugging Dataset Level of API Architecture

  18. Popularity TensorFlow PyTorch Keras

  19. • Rapid Prototyping • Small Dataset • Best for Newbies • Large Dataset • High Performance • Functionality • Flexibility • Training Duration • Debugging Capabilities

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