A recurrent neural network (RNN) may be a quite artificial neural network mainly utilized in speech recognition and natural language processing (NLP). RNN is employed in deep learning and within the development of models that imitate the activity of neurons within the human brain.
Recurrent Networks are designed to acknowledge patterns in sequences of data , like text, genomes, handwriting, the vocable , and numerical statistic data emanating from sensors, stock markets, and government agencies.
A recurrent neural network looks almost like a standard neural network except that a memory-state is added to the neurons. The computation is to incorporate an simple memory.
The recurrent neural network may be a sort of deep learning-oriented algorithm, which follows a sequential approach. In neural networks, we always assume that every input and output depend on all other layers. These sorts of neural networks are called recurrent because they sequentially perform mathematical computations.

Application of RNN

RNN has multiple uses when it involves predicting the longer term. within the financial industry, RNN can help predict stock prices or the sign of the stock exchange direction (i.e., positive or negative).
RNN is employed for an autonomous car because it can avoid a car accident by anticipating the route of the vehicle.
RNN is widely utilized in image captioning, text analysis, MT, and sentiment analysis. for instance, one should use a movie review to understanding the sensation the spectator perceived after watching the movie. Automating this task is extremely useful when the movie company cannot have longer to review, consolidate, label, and analyze the reviews. The machine can do the work with a better level of accuracy.
Following are the application of RNN:
1. Machine Translation
We make use of Recurrent Neural Networks in the translation engines to translate the text from one to another language. They do this with the combination of other models like LSTM (Long short-term memory) s.

2. Speech Recognition
Recurrent Neural Networks has replaced the normal speech recognition models that made use of Hidden Markov Models. These Recurrent Neural Networks, along side LSTMs, are better poised at classifying speeches and converting them into text without loss of context.

3. Sentiment Analysis
We make use of sentiment analysis to positivity, negativity, or the neutrality of the sentence. Therefore, RNNs are most adept at handling data sequentially to find sentiments of the sentence.

4. Automatic Image Tagger
RNNs, in conjunction with convolutional neural networks, can detect the images and provide their descriptions in the form of tags. For example, a picture of a fox jumping over the fence is better explained appropriately using RNNs.

Limitations of RNN

RNN is supposed to carry the information in time. However, it is quite challenging to propagate all this information when the time step is too long. When a network has too many deep layers, it becomes untrainable. This problem is called: vanishing gradient problem.
If we remember, the neural network updates the weight use of the gradient descent algorithm. The gradient grows smaller when the network progress down to lower layers.
The gradient stays constant, meaning there is no space for improvement. The model learns from a change in its gradient; this change affects the network’s output. If the difference in the gradient is too small (i.e., the weight changes a little), the system can’t learn anything and so the output. Therefore, a system facing a vanishing gradient problem cannot converge towards the right solution.
The recurrent neural will perform the following.
The recurrent network first performs the conversion of independent activations into dependent ones. It also assigns the same weight and bias to all the layers, which reduces the complexity of RNN of parameters. And it provides a standard platform for memorization of the previous outputs by providing previous output as an input to the next layer.
These three layers having the same weights and bias, combine into a single recurrent unit.

For calculating the current state-

ht =f(ht-1, Xt)
Where ht= current state
Ht-1= previous state
Xt= input state
To apply the activation function tanh, we have-
ht = tanh (Whhht-1+ WxhXt)
Where:
Whh = weight of recurrent neuron and,
Wxh = weight of the input neuron
The formula for calculating output:
Yt = Whyht

Training through RNN

• The network takes a single time-step of the input.
• We can calculate the current state through the current input and the previous state.
• Now, the current state through ht-1 for the next state.
• There is an number of steps, and in the end, all the information can be joined.
• After completion of all the steps, the final step is for calculating the output.
• At last, we compute the error by calculating the difference between actual output and the predicted output.
• The error is backpropagated to the network to adjust the weights and produce a better outcome.

So, this brings us to the end of blog. This Tecklearn ‘Recurrent Neural Network (RNN) in Tensor Flow’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Introduction to Deep Learning and AI
• What is Deep Learning?
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• Real-Life use cases of Deep Learning
• Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
• Pre-requisites for AI & DL
• Python Programming Language
• Installation & IDE
Environment Set Up and Essentials
• Installation
• Python – NumPy
• Python for Data Science and AI
• Python Language Essentials
• Python Libraries – Numpy and Pandas
• Numpy for Mathematical Computing
More Prerequisites for Deep Learning and AI
• Pandas for Data Analysis
• Machine Learning Basic Concepts
• Normalization
• Data Set
• Machine Learning Concepts
• Regression
• Logistic Regression
• SVM – Support Vector Machines
• Decision Trees
• Python Libraries for Data Science and AI
Introduction to Neural Networks
• Creating Module
• Neural Network Equation
• Sigmoid Function
• Multi-layered perception
• Weights, Biases
• Activation Functions
• Gradient Decent or Error function
• Epoch, Forward & backword propagation
• What is TensorFlow?
• TensorFlow code-basics
• Graph Visualization
• Constants, Placeholders, Variables
Multi-layered Neural Networks
• Error Back propagation issues
• Drop outs
Regularization techniques in Deep Learning
Deep Learning Libraries
• Tensorflow
• Keras
• OpenCV
• SkImage
• PIL
Building of Simple Neural Network from Scratch from Simple Equation
• Training the model
Dual Equation Neural Network
• TensorFlow
• Predicting Algorithm
Introduction to Keras API
• Define Keras
• How to compose Models in Keras
• Sequential Composition
• Functional Composition
• Predefined Neural Network Layers
• What is Batch Normalization
• Saving and Loading a model with Keras
• Customizing the Training Process
• Using TensorBoard with Keras
• Use-Case Implementation with Keras
GPU in Deep Learning
• Introduction to GPUs and how they differ from CPUs
• Importance of GPUs in training Deep Learning Networks
• The GPU constituent with simpler core and concurrent hardware
• Keras Model Saving and Reusing
• Deploying Keras with TensorBoard
Keras Cat Vs Dog Modelling
• Activation Functions in Neural Network
Optimization Techniques
• Some Examples for Neural Network
Convolutional Neural Networks (CNN)
• Introduction to CNNs
• CNNs Application
• Architecture of a CNN
• Convolution and Pooling layers in a CNN
• Understanding and Visualizing a CNN
RNN: Recurrent Neural Networks
• Introduction to RNN Model
• Application use cases of RNN
• Modelling sequences
• Training RNNs with Backpropagation
• Long Short-Term memory (LSTM)
• Recursive Neural Tensor Network Theory
• Recurrent Neural Network Model
Application of Deep Learning in image recognition, NLP and more
Real world projects in recommender systems and others

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