Activation functions provide neural networks with the ability to learn complex non linear relationships. Read along to find out the top 3 activation functions used in neural networks.
Building neural networks is a complex endeavor with many parameters to tweak prior to achieving the final version of a model. On top of this, the two most widely used numerical platforms for deep learning and neural network machine learning models, TensorFlow and Theano, are too complex to allow for rapid prototyping. The Keras Deep Learning library for Python helps bridge the gap between prototyping speed and the utilization of the advanced numerical platforms for deep learning. Keras is a high-level API for building neural networks that run on top of TensorFlow, Theano or CNTK. It allows for rapid prototyping, supports both recurrent and convolutional neural networks and runs on either your CPU or GPU for increased speed.