Should I learn TensorFlow or Keras?

They are not alternatives. Keras is the high-level API you write models with, and TensorFlow is what runs them. Learn Keras for building models, and enough TensorFlow to handle tensors, tf.data pipelines and deployment.

Why does tf.placeholder not exist?

Because it was removed in TensorFlow 2. Placeholders and tf.Session belong to TF1's graph-first design; TF2 runs eagerly, so you pass real tensors straight to functions. Any tutorial using them predates 2019 and needs replacing rather than patching.

Is TensorFlow still used in 2026?

Yes, particularly in production. PyTorch dominates research and papers, but TensorFlow's serving, mobile and browser deployment story keeps it common in industry, and a lot of existing systems run on it.

What is the difference between a tensor and a NumPy array?

For everyday use, very little — both are typed multi-dimensional arrays, and x.numpy() converts between them. The differences that matter are that tensors can live on a GPU and that TensorFlow records operations on them so it can compute gradients.

Do I need a GPU for TensorFlow?

Not to learn. Small models train fine on a CPU. A GPU becomes worthwhile for larger images, transformers or datasets that take hours — and Colab or Kaggle give you one free before you spend anything.

Why does TensorFlow not see my GPU?

Usually a version mismatch between TensorFlow, CUDA and cuDNN, or the CPU-only wheel being installed. Run tf.config.list_physical_devices('GPU') — an empty list means TensorFlow will silently use the CPU, so check your build and driver versions against the official compatibility table.

Tensorflow in Python Tutorials