Free Machine Learning Course: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=DescriptionFirstFol.. Recurrent Neural Network(RNN) are a type of Neural Network where the output from previous step are fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words. Thus RNN came into existence, which solved this issue with the help of a Hidden Layer. The. What is Recurrent Neural Network? Recurrent neural networks (RNNs) may be defined as the special breed of NNs that are capable of reasoning over time. RNNs are mainly used in scenarios, where we need to deal with values that change over time, i.e. time-series data. In order to understand it in a better way, let's have a small comparison between regular neural networks and recurrent neural networks Taking the simplest form of a recurrent neural network, let's say that the activation function is tanh, the weight at the recurrent neuron is Whh and the weight at the input neuron is Wxh, we can write the equation for the state at time t as - The Recurrent neuron in this case is just taking the immediate previous state into consideration We show how recurrent neural networks can be used for language mode... In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data
Specifying The Number Of Timesteps For Our Recurrent Neural Network. The next thing we need to do is to specify our number of timesteps.Timesteps specify how many previous observations should be considered when the recurrent neural network makes a prediction about the current observation.. We will use 40 timesteps in this tutorial. This means that for every day that the neural network predicts. In particular, the experiments in the post help visualise the internals of a recurrent neural network trained to generate handwriting. The truth is, that project also served as a kind of meta-experiment for myself. Rather than directly working on the visualisation experiments and writeup, I set out to create a pre-trained handwriting model with an easy-to-use Javascript interface, and have my collaborators, who are highly talented data visualisation artists, experiment with the model to. Recurrent neural networks is a type of deep learning-oriented algorithm, which follows a sequential approach. In neural networks, we always assume that each input and output is independent of all other layers. These type of neural networks are called recurrent because they perform mathematical computations in sequential manner Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be.
Tutorial on Recurrent Neural Networks from the Deep Learning Indaba 2018, held in Stellenbosch, South Africa A Beginner's Guide to LSTMs by AI.Wiki Language model tutorials Applications of Recurrent Neural Networks. This is the most amazing part of our Recurrent Neural Networks Tutorial. Below are some of the stunning applications of RNN, have a look - 1. Machine Translation. We make use of Recurrent Neural Networks in the translation engines to translate the text from one language to the other. They can do this. A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks Quoc V. Le qvl@google.com Google Brain, Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA 94043 October 20, 2015 1 Introduction In the previous tutorial, I discussed the use of deep networks to classify nonlinear data. In addition t Recurrent Neural Networks(RNNs) have been the answer to most problems dealing with sequential data and Natural Language Processing(NLP) problems for many years, and its variants such as the LSTM are still widely used in numerous state-of-the-art models to this date. In this post, I'll be covering the basic concepts around RNNs and implementing a plain vanilla RNN model with PyTorch to.
How To Build And Train A Recurrent Neural Network Table of Contents. Downloading the Data Set For This Tutorial. A set of test data that contains information on Facebook's stock price... Importing The Libraries You'll Need For This Tutorial. This tutorial will depend on a number of open-source. Recurrent Neural Network (RNN) Implementing Recurrent Neural Network with Keras. Importing and Preprocessing Data. Loading Data. Feature Scaling. Create Data Structure. Reshape. Create RNN Model. Prediction and Visualization of RNN Model
Recurrent Neural Network x RNN y We can process a sequence of vectors x by applying a recurrence formula at every time step: Notice: the same function and the same set of parameters are used at every time step
A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the echo state network approach - Semantic Scholar. This tutorial is a worked-out version of a 5-hour course originally held at AIS in September/October 2002. It has two distinct components Recurrent Neural Network are a type of Neural Network where the output from previous step are fed as input to the current step. In traditional neural networks, all the inputs and outputs are independent to each other, but when it is required to predict the next word of a sentence, the previous words are required and hence there is a requirement to remember the previous words
Recurrent neural networks 1.1 First impression There are two major types of neural networks, feedforward and recurrent. In feedforward networks, activation is piped through the network from input units to output units (from left to right in left drawing in Fig. 1.1) In this tutorial, you will discover a suite of 5 narrowly defined and scalable sequence prediction problems that you can use to apply and learn more about LSTM recurrent neural networks. After completing this tutorial, you will know: Simple memorization tasks to test the learned memory capability of LSTMs In this post, you will discover how you can develop LSTM recurrent neural network models for sequence classification problems in Python using the Keras deep learning library. After reading this post you will know: How to develop an LSTM model for a sequence classification problem. How to reduce overfitting in your LSTM models through the use of dropout. How to combine LSTM models with. We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. However, compared to..
Neural Network Tutorials - Herong's Tutorial Examples ∟ RNN (Recurrent Neural Network) This chapter provides introductions and tutorials on RNN (Recurrent Neural Network). Topics include introduction to the classical RNN model, LSTM (Long Short-Term Memory) model, GRU (Gated Recurrent Unit) model. What Is RNN (Recurrent Neural Network This post is inspired by recurrent-neural-networks-tutorial from WildML. And you can deeply read it to know the basic knowledge about RNN, which I will not include in this tutorial. In this tutorial, we will focus on how to train RNN by Backpropagation Through Time (BPTT), based on the computation graph of RNN and do automatic differentiation The structure of Recurrent Neural Network Now with this basic intuition, let's go deeper into the structure of RNN. This is a simple RNN with one shallow layer. Our model is now going to take two values: the X input value at time t and the output value A from the previous cell (at time t-1)
Recurrent neural networks exemplified by the fully recurrent network and the NARX model have an inherent ability to simulate finite state automata. Automata represent abstractions of information processing devices such as computers. The computational power of a recurrent network is embodied in two main theorems: Theorem 1 All Turing machines may be simulated by fully connected recurrent. And that's what I'll showcase in this tutorial. This article assumes a basic understanding of recurrent neural networks. In case you need a quick refresher or are looking to learn the basics of RNN, I recommend going through the below articles first: Fundamentals of Deep Learning; Introduction to Recurrent Neural Networks . Table of Contents. Flashback: A Recap of Recurrent Neural Network. As part of the tutorial we will implement a recurrent neural network based language model. The applications of language models are two-fold: First, it allows us to score arbitrary sentences based..
This tutorial demonstrates how to generate text using a character-based RNN. You will work with a dataset of Shakespeare's writing from Andrej Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks.Given a sequence of characters from this data (Shakespear), train a model to predict the next character in the sequence (e) A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the loop: An unrolled recurrent neural network. This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists As part of the tutorial we will implement a recurrent neural network based language model. The applications of language models are two-fold: First, it allows us to score arbitrary sentences based on how likely they are to occur in the real world. This gives us a measure of grammatical and semantic correctness. Such models are typically used as part of Machine Translation systems. Secondly, a. Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano Resource Recurrent neural network architectures have been used in tasks dealing with longer term dependencies between data points. We investigate these architectures to overcome the difﬁculties arising from learning policies with long term dependencies. 1 Introduction Recent advances in reinforcement learning have led to human-level or greater performance on a wide variety of games (e.g. Atari 2600.
This tutorial is intended for someone who wants to understand how Recurrent Neural Network works, no prior knowledge about RNN is required. We will implement the most simple RNN model - Elman Recurrent Neural Network. To get a better understanding of RNNs, we will build it from scratch using Pytorch tensor package and autograd library. I assume that [ Neural Network Taxonomy: This section shows some examples of neural network structures and the code associated with the structure. First, a couple examples of traditional neural networks will be shown. This form of network is useful for mapping inputs to outputs, where there is no time-dependent component. In other words, the knowledge of past events is not predictive of future events. The. Tutorials on getting started with PyTorch and TorchText for sentiment analysis. RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as.
In this tutorial you have learned to create, train and test a four-layered recurrent neural network for stock market prediction using Python and Keras. Finally, we have used this model to make a prediction for the S&P500 stock market index. You can easily create models for other assets by replacing the stock symbol with another stock code. A list of common symbols for stocks or index fonds is. In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN). For this purpose, we will train and evaluate models for time-series prediction problem using Keras. For GA, a python package called DEAP will be used
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented. In this tutorial, you had your first introduction to recurrent neural networks. More specifically, we discussed the intuition behind recurrent neural networks. Here is a brief summary of what we discussed in this tutorial: The types of problems solved by recurrent neural networks; The relationships between the different parts of the brain and the different neural networks we've studied in this. Recursive Neural Network uses a tree structure with a fixed number of branches. In the case of a binary tree, the hidden state vector of the current node is computed from the hidden state vectors.. Recurrent Neural Network (RNN) Tutorial for Beginners Lesson - 14. The Best Introduction to Deep Learning - A step by step Guide Lesson - 15. What Is Keras? The Best Introductory Guide to Keras Lesson - 16. Artificial intelligence and machine learning haven't just grabbed headlines and made for blockbuster movies; they're poised to make a real difference in our everyday lives, such as with.
Recurrent Neural Networks (RNNs) In this tutorial we are going to look at Recurrent Neural Networks and time series data. In future videos, we are going to show how to take these RNNs and apply them to text data. Timeseries Data. First of all, what is time series data? In the real world, data changes over time. For instance, if we look at this famous dataset of airplane sales from 1949 - 1969. A recurrent neural network (RNN) is a class of neural networks that includes weighted connections within a layer (compared with traditional feed-forward networks, where connects feed only to subsequent layers). Because RNNs include loops, they can store information while processing new input. This memory makes them ideal for processing tasks where prior inputs must be considered (such as time. Recurrent neural networks (RNNs) RNN is a multi-layered neural network that can store information in context nodes, allowing it to learn data sequences and output a number or another sequence. In simple words it an Artificial neural networks whose connections between neurons include loops. RNNs are well suited for processing sequences of inputs The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work. Below topics are explained in this recurrent neural networks tutorial: 1
Recurrent Neural Network (RNN) in TensorFlow. A recurrent neural network (RNN) is a kind of artificial neural network mainly used in speech recognition and natural language processing (NLP).RNN is used in deep learning and in the development of models that imitate the activity of neurons in the human brain.. Recurrent Networks are designed to recognize patterns in sequences of data, such as. This Edureka Recurrent Neural Networks tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short. Advanced Recurrent Neural Networks. Recurrent Neural Networks (RNNs) are used in all of the state-of-the-art language modeling tasks such as machine translation, document detection, sentiment analysis, and information extraction. Previously, we've only discussed the plain, vanilla recurrent neural network
This Recurrent Neural Network tutorial will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case [ Echo state networks (ESN) provide an architecture and supervised learning principle for recurrent neural networks (RNNs). The main idea is (i) to drive a random, large, fixed recurrent neural network with the input signal, thereby inducing in each neuron within this reservoir network a nonlinear response signal, and (ii) combine a desired output signal by a trainable linear combination of. Recurrent Neural Networks (RNNs) In this tutorial we are going to look at Recurrent Neural Networks and time series data. In future videos, we are going to show how to take these RNNs and apply them to text data. Timeseries Data. First of all, what is time series data? In the real world, data changes over time. For instance, if we look at this famous dataset of airplane sales from 1949 - 1969 we can see that there is a general trend upwards, and a cyclical trend between years. In order to.
Finally, we will have an extra tutorial on LSTM variations just to get you up to speed on what other options of LSTM exist out there in the world. Ready to get started? Then move on to our next section! The Idea Behind Recurrent Neural Networks (For the PPT of of this lecture Click Here) Recurrent Neural Networks represent one of the most advanced algorithms that exist in the world of. Summary: Recurrent Neural Networks, RNN, LSTM, Long Short-term Memory, seq2seq. Toggle navigation Machine Learning Tutorial. Nav; GitHub; ml_tutorial. Overview; Introduction to MLE and MAP; Naive Bayes Classifier; Linear Regression; Logistic Regression; Neural Network; Convolutional Neural Networks; Autoencoders; Text Search using TF-IDF and Elasticsearch ; Recurrent Neural Networks; Sentiment. Recurrent Neural Networks (RNNs)! • Recurrent Neural Networks take the previous output or hidden states as inputs. ! The composite input at time t has some historical information about the happenings at time T < t! • RNNs are useful as their intermediate values (state) can store information about past inputs for a time that is no Recurrent Neural Networks Recurrent Neural Networks (RNNs) o↵er several advantages: Non-linear hidden state updates allows high representational power. Can represent long term dependencies in hidden state (theoretically). Shared weights, can be used on sequences of arbitrary length. Recurrent Neural Networks (RNNs) 5/2
Recurrent neural networks (RNNs) are neural nets that can deal with sequences of variable length (unlike feedforward nets). They are able to this by defining a recurrence relation over timesteps which is typically the following formula: Sk = f (Sk−1 ⋅W rec +Xk ⋅W x) S k = f (S k − 1 ⋅ W r e c + X k ⋅ W x In this tutorial, you had your first introduction to recurrent neural networks. More specifically, we discussed the intuition behind recurrent neural networks. Here is a brief summary of what we discussed in this tutorial: The types of problems solved by recurrent neural networks Recurrent Neural Networks ¶ In a recurrent neural network we store the output activations from one or more of the layers of the network. Often these are hidden later activations. Then, the next time we feed an input example to the network, we include the previously-stored outputs as additional inputs Recurrent neural networks are very useful when it comes to the processing of sequential data like text. In this tutorial, we are going to use LSTM neural networks (Long-Short-Term Memory) in order to tech our computer to write texts like Shakespeare
Recurrent Neural Networks Tutorial, Part 2 - Implementing a RNN with Python, Numpy and Theano; Posted by Huiming Song Sun 20 August 2017 Python python, deep learning. Recent Posts. 2019-07-27 Week 30 工具合集 ; 2019-07-20 Week 29, nvidia-smi error; 2019-06-22 Week 25 工具合集; 2019-06-15 Week 24; 2019-06-08 Week 23 Regular Expression to clean data; Categories. Linux; python; Rthers. Recurrent neural networks tutorial Using Neural Networks for Regression: Radial Basis Function Networks. 28/03/2021 28/10/2020 by Mohit Deshpande. Neural Networks are very powerful models for classification tasks. But what about regression? Suppose we had a set of data points and wanted to project that trend into the future to make predictions. Regression has many applications in finance. Title: Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network. Authors: Alex Sherstinsky. Download PDF Abstract: Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides. However, in most articles, the inference formulas for the LSTM. Introduction to Recurrent Neural Networks. Recurrent Neural Networks (RNNs) are a kind of neural network that specializes in processing sequences. RNNs are often used in Natural Language Processing (NLP) tasks because of their effectiveness in handling text This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, applications, and how to bring the models to life using Keras. In this tutorial we'll start by looking at deep RNNs. Specifically, we'll cover: The Idea: Speech Recognition; Why.
In this tutorial we will show how to train a recurrent neural network on a challenging task of language modeling. The goal of the problem is to fit a probabilistic model which assigns probabilities to sentences. It does so by predicting next words in a text given a history of previous words. For this purpose we will use the Penn Tree Bank (PTB) dataset, which is a popular benchmark for. Recurrent neural networks are a type of neural network that add the explicit handling of order in input observations. This capability suggests that the promise of recurrent neural networks is to learn the temporal context of input sequences in order to make better predictions. That is, that the suite of lagged observations required to make a prediction no longer must be diagnosed and specified as in traditional time series forecasting, or even forecasting with classical neural networks. In this part of the series, we will introduce Recurrent Neural Networks aka RNNs that made a major breakthrough in predictive analytics for sequential data. This article covers RNNs on both conceptual and practical levels. We will start with the definition of RNNs, why and when they are used, then we will build an RNN ourselves for sentiment analysis
Recurrent Neural Networks Introduction. Take a look at this great article for an introduction to recurrent neural networks and LSTMs in particular. Language Modeling. In this tutorial we will show how to train a recurrent neural network on a challenging task of language modeling. The goal of the problem is to fit a probabilistic model which assigns probabilities to sentences. It does so by. The idea behind Bidirectional Recurrent Neural Networks (RNNs) is very straightforward. Which involves replicating the first recurrent layer in the network then providing the input sequence as it is as input to the first layer and providing a reversed copy of the input sequence to the replicated layer. This overcomes the limitations of a traditional RNN.Bidirectional recurrent neural network (BRNN) can be trained using all available input info in the past and future of a particular time-step This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in And that's where recurrent neural networks come in, that's the gap that they fill in. And so, let's have a look at a couple of examples. A huge shout to the Karpathy blog, karpathy.github.io, some of these examples are from here. So, one to many relationships, this is when you have one input and have multiple outputs. An example of this is an image where a computer describes the image. So, you. Recurrent Neural Networks (RNN) basically unfolds over time. It is used for sequential inputs where the time factor is the main differentiating factor between the elements of the sequence. For example, here is a recurrent neural network used for language modeling that has been unfolded over time. At each time step, in addition to the user input at that time step, it also accepts the output of.
In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN. We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs. Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from. This Edureka Recurrent Neural Networks tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo. Recurrent neural networks are similar to Turing Machine. It is invented in the 1980s. Equation of RNN. ht = fw(ht-1,) where ht = new state, ht-1= previous state, fw = activation function, xt = input vector Figure 1: Vanilla Architecture. The above structure gives the basic idea behinds the RNN functionality. This structure is very famous and it is known as Vanilla Architecture. This design. This tutorial, along with the following two, Before autograd, creating a recurrent neural network in Torch involved cloning the parameters of a layer over several timesteps. The layers held hidden state and gradients which are now entirely handled by the graph itself. This means you can implement a RNN in a very pure way, as regular feed-forward layers. This RNN module (mostly copied.
A Tutorial on Quantum Graph Recurrent Neural Network (QGRNN) Abstract: Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors assigned to nodes and edges have been attracting attention in various fields LSTM recurrent neural network applications by (former) students & postdocs: 1. Recognition of connected handwriting : our LSTM RNN (trained by CTC) outperform all other known methods on the difficult problem of recognizing unsegmented cursive handwriting; in 2009 they won several handwriting recognition competitions (search the site for Schmidhuber's postdoc Alex Graves )
where \(\eta\) is the learning rate which controls the step-size in the parameter space search. \(Loss\) is the loss function used for the network. More details can be found in the documentation of SGD Adam is similar to SGD in a sense that it is a stochastic optimizer, but it can automatically adjust the amount to update parameters based on adaptive estimates of lower-order moments Recurrent Neural Networks (RNN) Tutorial | Tensorflow Tutorial | Deep Learning Rewind Course Online. 30 day money back guarantee Course language: English. This course covers: Unlimited access forever Access on computers, tablets, phones and TV sets. Requirements. Access to a computer, tablet, phone or TV with internet connection. Description. All our premium courses are created by the best.
In this article, we will use the power of RNN (Recurrent Neural Networks), LSTM (Short Term Memory Networks) & GRU (Gated Recurrent Unit Network) and predict the stock price. We are going to use TensorFlow 1.12 in python to coding this strategy Package 'rnn' July 3, 2020 Title Recurrent Neural Network Version 1.4.0 Description Implementation of a Recurrent Neural Network architectures in native R, including Long Short In this section of the Machine Learning tutorial you will learn about artificial neural networks, biological motivation, weights and biases, input, hidden and output layers, activation function, gradient descent, backpropagation, long-short term memory, convolutional, recursive and recurrent neural networks
I am quite new to the Caffe framework, only recently starting to use it. I understand that modelling CNNs is allowed, however, is it possible to combine RNNs (not much experience with these) and C.. The aim of this tutorial is to show the use of TensorFlow with KERAS for classification and prediction in Time Series Analysis. The latter just implement a Long Short Term Memory (LSTM) model (an instance of a Recurrent Neural Network which avoids the vanishing gradient problem). Introduction The code below. Tutorial. Build a recurrent neural networks using TensorFlow Keras Understand how TensorFlow builds and executes an RNN model for language modeling. Save. Like. By Sidra Ahmed, Sandhya Nayak Published March 10, 2021. Language modeling is the task of assigning probabilities to sequences of words, and is one of the most important tasks in natural language processing. Given the context of one.
Søg efter jobs der relaterer sig til Recurrent neural network tutorial, eller ansæt på verdens største freelance-markedsplads med 19m+ jobs. Det er gratis at tilmelde sig og byde på jobs Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence. Nonetheless, popular tasks such as speech or images recognition, involve multi-dimensional input features that are characterized by strong internal dependencies between the dimensions of the input. Long Short-Term Memory: Tutorial on LSTM Recurrent Networks. Tutorial covers the following LSTM journal publications: Even static problems may profit from recurrent neural networks (RNNs), e.g., parity problem: number of 1 bits odd? 9 bit feedforward NN: Parity problem, sequential: 1 bit at a time. Other sequential problems. Other sequence. Video Tutorial: Introduction to Recurrent Neural Networks in TensorRT. By Nefi Alarcon. Tags: Machine Learning & Artificial Intelligence, News, TensorRT. Discuss NVIDIA TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency and high-throughput. TensorRT can import trained models from every deep learning framework to easily create highly efficient. Recurrent neural networks (RNN's) are used when the input is sequential in nature. Typically RNN's are much more effective than regular feed forward neural networks for sequential data because they can keep track of dependencies in the data over multiple time steps. This is possible because the output of a RNN at a time step depends on the current input and the output of the previous time. GRU 是 gated recurrent units 的缩写，由 Cho在 2014 年提出GRU 和 LSTM 最 的不同在于 GRU 将遗忘门和输入门合成了一个更新门，同时网络不再额外给出记忆状态，而是将输出结果作为记忆状态不断向后循环传递，网络的输人和输出都变得特别简单