It’s easy for deep learning newbies to get started with neon! Neon’s Python-like syntax includes object-oriented implementations of all the deep learning components, including layers, learning rules, activations, optimizers, initializers, and costs functions. And out-of-the-box examples cover all the standard deep learning use cases, including image recognition, speech, video, and natural language processing. So even those new to deep learning can easily implement all the common deep learning models in neon, including convnets, MLPs, RNNs, LSTMs and autoencoders. Meanwhile, more experienced deep learning data scientists can create their own novel algorithms using linear algebra, auto-differentiation, and other advanced capabilities with a numpy-like syntax.