PyTorch Vs TensorFlow

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A widely used ML library that offers an automatic differentiation library for implementing neural nets & is popular for computer vision & NLP applications.

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1. Availability via Cloud 2. Dynamic Computational Graphs 3. Efficient Model Deployment 4. Huge and Proactive Community 5. Distributed Training

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A powerful ML library that prioritizes the training of deep neural networks and is widely used for deep learning applications, including image processing and video detection.

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1. Create Complex Topologies 2. High Extensibility 3. Distribution Strategy API 4. More Flexibility with Eager Execution 5. Read-to-deploy Anywhere

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- PyTorch is used for computer vision, reinforcement learning, and natural language processing. - TensorFlow specializes in training and inference of deep neural nets.

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- PyTorch uses an imperative and dynamic approach to graph definition. - TensorFlow uses a static approach to define graphs.

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- PyTorch is tightly integrated with Python & provides built-in support for it. - TensorFlow doesn't provide special support for Python.

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- PyTorch allows debugging at runtime using Python debugging tools like pdb and PyCharm debugger. - TensorFlow only has runtime debugging available with tfdgb.

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Ready to dive into the popular libraries for machine learning?

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PyTorch Vs TensorFlow

A Side-by-Side comparison of popular ML libraries...

A widely used ML library that offers an automatic differentiation library for implementing neural nets & is popular for computer vision & NLP applications.

What is PyTorch?

1. Availability via Cloud 2. Dynamic Computational Graphs 3. Efficient Model Deployment 4. Huge and Proactive Community 5. Distributed Training

Features of PyTorch

A powerful ML library that prioritizes the training of deep neural networks and is widely used for deep learning applications, including image processing and video detection.

What is TensorFlow?

1. Create Complex Topologies 2. High Extensibility 3. Distribution Strategy API 4. More Flexibility with Eager Execution 5. Read-to-deploy Anywhere

Features of TensorFlow

- PyTorch is used for computer vision, reinforcement learning, and natural language processing. - TensorFlow specializes in training and inference of deep neural nets.

Comparison based on Primary Use

- PyTorch uses an imperative and dynamic approach to graph definition. - TensorFlow uses a static approach to define graphs.

Comparison based on Approaches to graph definition

- PyTorch is tightly integrated with Python & provides built-in support for it. - TensorFlow doesn't provide special support for Python.

Comparison based on Python Friendliness

- PyTorch allows debugging at runtime using Python debugging tools like pdb and PyCharm debugger. - TensorFlow only has runtime debugging available with tfdgb.

Comparison based on Debugging

Ready to dive into the popular libraries for machine learning?

Explore the differences in detail....

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