Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What you need to know to train recurrent neural networks to make Flip
  Flops memories and more   Training neural networks to perform different tasks is relevant across
various disciplines that go beyond Machine Learning. In particular, Recurrent
Neural Networks (RNN) are of great interest to different scientific
communities, for example, Computational Neuroscience research and Dynamical
Systems among others. Open-source frameworks dedicated to Machine Learning such
as Tensorflow and Keras has produced significant changes in the development of
technologies that we currently use. One relevant problem that can be approached
is how to build the models for the study of dynamical systems, and how to
extract the relevant information to be able to answer the scientific questions
of interest. The purpose of the present work is to contribute to this aim by
using a temporal processing task, in this case, a 3-bit Flip Flop memory, to
show the modeling procedure in every step: from equations to the software code
using Tensorflow and Keras. The obtained networks are analyzed to describe the
dynamics and to show different visualization and analysis tools. The code
developed in this work is provided to be used as a base for model other
systems.

22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg