Learn TENSORFLOW with Real Code Examples

AI-powered apps and services

Image/text analysis SaaS

Recommendation engines

Predictive analytics solutions

Licensing models for trained networks

Enhanced TPU acceleration

Expanded pre-trained model support

Integration with AutoML workflows

Improved deployment options

More tutorials and community examples

Small or trivial ML tasks

Non-Python environments without TF support

Ultra-low-latency embedded applications without TensorFlow Lite

Highly experimental research requiring custom frameworks

Learning-only scenarios where simplicity is key (Keras might suffice)

TensorFlow is a versatile, open-source ML platform from Google.

It supports training, evaluation, and deployment of ML models across platforms.

Best suited for scalable, production-ready AI applications.

Integration with Keras simplifies model building for beginners.

Offers tools for cloud, mobile, and web deployment.

Is TensorFlow free?

Yes - open-source under Apache 2.0 license.

Does it support GPUs?

Yes - via CUDA/cuDNN and TPU acceleration.

Which platforms are supported?

Windows, macOS, Linux, Cloud, Mobile (iOS/Android).

Is it beginner-friendly?

Moderately - Keras simplifies usage for beginners.

Can it run on mobile?

Yes - via TensorFlow Lite.