There are all sorts of options for learning modern natural language processing, notably those options with neural networks techniques. For example, there is freely-available course material from world class universities such as Stanford's Natural Language Processing with Deep Learning, among others. There are also courses, paid and otherwise, from independent non-university sources such as Coursera and fast.ai. There is a wide variety of quality books that have been published over the recent few years which are topical and up-to-date. Today, if you want to learn modern natural language processing techniques, there is no excuse for not doing so.

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM. Our framework allows to compare learned algorithms to hand-made algorithms within the traditional train and test methodology. In an experiment, we learn a learning algorithm for a one-hidden layer Multi-Layer Perceptron (MLP) on non-linearly separable datasets. The learned algorithm is able to update parameters of both layers and generalise well on similar datasets.