Within the scope of this article, the concept of LSTMs is going to be dwelt upon. The acronym stands for “long short-term memory (LSTM) units”. It is considered to be a more general and useful version of GRU, which stands for Gated Recurrent Unit in neural networks, allowing them to learn quicker and acquire new pieces of information easier than it has been possible several years ago. Thanks to the constant development of such solutions and LSTM network or GRU, it is possible to utilize neural networks in a variety of modern and state-of-the-art applications, some of which are speech recognition, machine translation, and possibly the most interesting of them all, namely – natural speech understanding, which as can be easily concluded, is an exceptionally demanding task requiring a breathtaking processing power and properly adjusted algorithms.
Let us now go back to LSTM (memory). It takes us a step closer to truly sentient neural networks that work similarly to human brain. It has to be pointed here that we learn through experience, meaning that we draw certain conclusions from events and phenomena we experienced in the past. “Memory” in LSTM is capable of performing a similar feat, for it can remember previous pieces of information and expand its neural database. Thanks to that, the so-called long-term dependencies are created. LSTM is also taken advantage of to make neural networks much more reliable and generate less errors, especially due to the fact that it can utilize gates in order to control the entirety of the so-called memorizing process.
Long Short-Term Memory
LSTM (memory) is considered by scientist and programmers to be the go-to solution when it comes to allowing RNNs (Recurrent Neural Networks) to learn and develop basing on long-term dependencies. It has been proved to outperform and overshadow other methods utilized to date, as well as currently has no competitors when it comes to both performance and reliability. Interestingly enough, the “memory” in LSTM may be programmed in such a way to keep specific values of set parameters for either a long or a short period of time, depending on the application. As it can be easily guessed, when it comes to speech understanding, the latter method is more desirable, for it allows to quickly decode the input message and provide a given value, whereas while taking into account such applications as machine translation, the LSTM network should take advantage of a vast database of values stored for a long period of time in order to ensure the highest reliability of provided translation and the lowest number of errors possible.
With time, the long short-term memory has become so powerful, that it is currently utilized for a myriad of applications that are far more complex than a simple process of taking advantage of loops allowing the network to utilize provided information to perform a given task (typically: to provide the user with a desired value of a highly specific parameter). Thanks to the development of technology, software, and available programming tools, LSTM can be used to recognize human actions, to properly understand messages presented via sign language, as well as to recognize, identify, and classify handwriting. Some labs and institutes also adjust LSTM (memory) algorithms to help them deal with protein homology detection and predicting their location in subcellular networks. It is believed and highly anticipated that further improvements in the capabilities and functioning of LSTM will help professionals in many fields of science and industry work more efficiently and achieve more homogenous results of their undertakings.
Recurrent Neural Networks
As of now, it is clear that the LSTM network is the most advanced set of units that can be utilized to manage and optimize the performance of Recurrent Neural Networks. It is difficult to state whether or not it will be replaced by a more polished and powerful tool in a foreseeable future, because no better solution has been invented and implemented as of currently. That is why it is also the most popular one amongst scientists and programmers, quickly overshadowing GRU and concepts alike. It is, however, surprising to what an extent a simple idea proposed by German scientists in 1997 has been developed, improved, and how many errors and faults in operation have been removed in order to ensure its accurate and reliable performance. One thing can be said for sure, the enhancement of the LSTM (memory) technology will be exceptionally interesting to observe and keep track of.