Recurrent Neural Networks have vast applications in image classification and video recognition, machine translation, and music composition.
Consider an image classification use-case where we have trained the neural network to classify images of some animals.
So, let’s feed an image of a cat or a dog; the network provides an output with the corresponding label to the picture of a cat or a dog.
See the below diagram:

Here, the first output being a cat will not influence the previous output, which is a dog. This means that output at a time ‘t’ is autonomous of output at the time ‘t-1?.
Consider the scenario where we will require the use of the last obtained output:

The concept is the same as reading a book. With every page we move forward into, we need the understanding of previous pages to make complete sense of the information in most of the cases.
With the help of the feed-forward network, the new output at the time ‘t+1? has no relation with outputs at either time t, t-1, t-2.
So, the feed-forward network cannot be used when predicting a word in a sentence as it will have no absolute relation with the previous set of words.
But, with the help of Recurrent Neural Networks, this challenge can be overcome.
See the following diagram:

In the above diagram, we have specific inputs at-1? which is fed into the network. These inputs will lead to parallel outputs at time ‘t-1’ as well.
In the next timestamp, information from the previous input’t-1? is provided along with input at’t? to provide the output at ‘t eventually.’
This process repeats itself, to ensure that the latest inputs are aware and can use the information from the previous timestamp is obtained.
The recurrent network is a type of artificial neural network which is designed to recognize patterns in sequences of data. Like, text, genomes, handwriting, the spoken word, numerical times series data from sensors, stock markets, and government agencies.
For better understanding, consider the following comparison:
We go to the gym regularly, and the trainer has given us the schedule of our workout:

Note that all the exercises are repeated in a proper order every week. Let us use a feed-forward network to trying and predicting the types of exercises.

The inputs are the day, month, and health status. A neural network has been trained using these inputs to provide the prediction of the exercise.
However, this will not very accurate, considering the input. To fix this, we make use of the concept of Recurrent Neural Networks, as shown below:

In this case, find the inputs to be workout done on the previous day.
So if we did a shoulder exercise yesterday, we could do a bicep exercise today, and this goes on for the rest of the week.
However, if we happen to miss a day at the gym, the data from the previously attended timestamp can be considered below.

If a model is experienced based on the data it obtains from the last exercises, the output from the model will be accurate.
To sum it, let us convert the data into vectors.
Where vectors are numbers which are input to the model to denote if we have done the exercise or not.

So, if we have a shoulder exercise, the corresponding node will be ‘1’, and the rest of the exercise nodes will be mapped to ‘0’.
We have to check the math behind the working of the neural network.

examine ‘w’ to be the weight matrix and ‘b’ as the bias:
At time t=0, the input is ‘x0, and the task is to figure out what is ‘h0’. Substituting t=0 in the equation and acquiring the function h(t) value. The next value of ‘y0’ is finding out using the previously calculated values when applied to the new formula.
The same process is repeated through all of the timestamps in the model to train a model.

Training Recurrent Neural Networks

Recurrent Neural Networks use a backpropagation algorithm for training, but it is applied for each timestamp. It is commonly known as Back-propagation by Time (BTT).
Some issues with Back-propagation, such as:
• Vanishing Gradient
• Exploding Gradient
Vanishing Gradient
In Vanishing Gradient’s use of backpropagation, the goal is to calculate the error, which is found out by finding out the difference between the actual found out by finding out the difference between the actual output and the model output and raising that to a power of 2.

With the calculated error, the changes in the mistake concerning the difference in the weight are estimated. But with each learning rate, this can multiply with the same model.
So, Product of the learning rate with the change pass to the value, which is a definite change in weight.
The change in weight is added to the old set of weights for every training iteration, as shown in the below figure. The assign here is when the change in weight is multiplied, and then the value is less.
Exploding Gradient
The working of the collapse gradient is similar, but the weights here change extremely instead of negligible change. Notice the small here:

We have to overcome both of these, and it is some challenge at first.

Exploding gradients	Vanishing gradients
Truncated BTT Instead of starting backpropagation at the last timestamp, we can choose a smaller timestamp like 10	ReLU activation function We can use activation like ReLU, which gives output one while calculating the gradient
Clip gradients at the threshold Clip the gradient when it goes more than a threshold	RMSprop Clip the gradient when it goes higher than a threshold
RMSprop to adjusting the learning rate	LSTM, GRUs The different network architecture that has been specially designed can be used to combat this problem

What are long-term dependencies?

Many times, only recent data is desired in a model to performing the operations. But there may be a requirement from a piece of information which was obtained in the past.
So, this brings us to the end of blog. This Tecklearn ‘Working of 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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What you will Learn in this Course?

Introduction to Deep Learning and AI
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• 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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