Numpy allows fast computation of N-dimensional arrays since it stores values in memory the native way instead of Python lists.
Scikit-learn is useful that it provides some ready-to-use unsupervised learning algorithms, provides also SVM (Support Vector Machine).
Google TensorFlow is the most popular gradient-based machine learning library. It is designed as a base for Keras. AI engineers may use Keras APIs or the raw computation power of TensorFlow. TensorFlow has the capability of running parallelly on multiple CPUs, multiple GPUs, and even multiple GPUs on multiple machines.
PyTorch is popular, used by big companies such as Tesla and Facebook. However, there’s something unfundamental in it, the activation function is outside of neuron and layer, but the activation function should be inside as per biological structure of neuron.