TensorFlow is better than Keras when you need more control, deep customisation, or to handle large models at scale. Keras is easy and quick for simple models, but TensorFlow lets you tweak low-level details and achieve advanced performance. TensorFlow has built-in tools for distributed training, GPU/TPU use, and smooth deployment (such as TensorFlow Serving and Lite). It’s a better choice if you want to optimise, scale, or deeply integrate your AI beyond the basics.
Related TensorFlow Questions And Answers
- Is TensorFlow only for Python, or does it support other languages?
- What is TensorFlow used for?
- Why is PyTorch better than TensorFlow?
- How is TensorFlow used in machine learning?
- TensorFlow vs PyTorch: which is better for production ML?
- TensorFlow vs Keras: which is better for rapid prototyping?
- TensorFlow vs JAX: which is better for research and performance?
- How do TensorFlow developers optimize model performance and scalability?
- How does TensorFlow compare to MXNet for large-scale distributed training?
- What are the key advantages of TensorFlow over Caffe in machine learning?
- What is TensorFlow's role in deploying machine learning models to production environments?
- How to implement neural networks using TensorFlow?
- What are TensorFlow datasets (TFDS) and how are they used?
- What are TensorFlow's APIs for handling image and text data?
- How does TensorFlow train and optimize machine learning models?
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