Which library is best for AI in Python?

1. Deep Learning (Production): TensorFlow
Strong support for deploying models on servers, mobile devices (TensorFlow Lite), and browsers (TensorFlow.js).
Backed by Google Cloud and great for scaling enterprise AI.
2. Deep Learning (Research): PyTorch
**Its dynamic graph execution is more natural in Python.
Preferred by academics, researchers, and most modern AI papers.
**3. Classical ML: Scikit-learn
**Best for regression, classification, clustering, feature engineering, small to medium datasets.
**4. Quick Prototyping: Keras
**Easy syntax, fewer lines of code and often acts as a “friendly front-end” to TensorFlow.
**5. NLP & Gen AI: Hugging Face
Access to thousands of pre-trained models for text, images, and audio.
**Is TensorFlow better than PyTorch?
**TensorFlow is the best option for Better for large-scale deployment, mobile/web support and production pipelines. It is more common in enterprise & industry deployment.

• Production-ready: Easier to deploy at scale.
• TensorFlow Lite & TensorFlow.js: great for mobile apps & web apps.
• Rich ecosystem: TensorBoard for visualization, TensorFlow Extended for pipelines.
• *Corporate adoption is high *(Google, Airbnb, Twitter).

PyTorch is easier to learn, more flexible for research, and dominant in academia. It is more common in research labs & universities.

• Pythonic & intuitive: Code feels like normal Python.
• Research dominance: Most cutting-edge AI papers & models are released in PyTorch first.
• Large community of AI researchers: better support for trying new architectures.
• TorchScript & ONNX allow deployment too.