Neural network projects typically use some of the few architectural approaches like RNN, CNN, NTM, LSTM.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts.
Neuroevolution is a subfield within artificial intelligence (AI) and machine learning (ML) that consists of trying to trigger an evolutionary process similar to the one that produced our brains, except inside a computer. In other words, neuroevolution seeks to develop the means of evolving neural networks through evolutionary algorithms.
Generative adversarial networks (GANs) are a class of neural networks more recently used in unsupervised machine learning. They facilitate a wide class of handy applications such as retrieving images that contain a given pattern or make predictions for a better medication to a certain disease.
APIs: Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Uses TensorFlow back-end engine by default.