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Networks inspired by the human brain!
Artificial intelligence has now spread its wings in multiple sectors, especially by integrating advanced techniques like deep learning, machine learning, and neural networks.
Inspired by the human brain, deep neural networks work beyond just focusing on “if-else” conditions. These networks are designed to analyse, predict and provide solutions to problems.
The best part about these networks is that you do not need any coding or programming to obtain your desired output. Though, here, the results are provided on the basis of experiences and learnings like a normal human would do!
We use neural networks in our daily lives and we do not even realise it! Common applications like Google Assistant, Amazon product recommendations, Siri, Alexa and FaceApp, all such applications work on the concept of neural networks.
But what is a deep neural network? How does it work? What do you mean by greedy layer-wise training of deep networks?
All such questions are obvious especially when you are new to this concept. However, to help you get rid of all your confusion, we have come up with this guide to help you understand everything about deep networks.
What Is A Deep Neural Network?
Deep neural networks are simply sets of algorithms which are designed to analyse trends and patterns. With the help of machine perceptions, clustering and labelling, these networks are used to interpret the sensory data.
Such networks recognise numerical and vector-based patterns into which the real-life data should be translated. These networks help you cluster and classify the data.
To understand in simpler terms, you can consider the example of framing in the data link layer. In framing, the data is divided into discernible blocks which are then transmitted as a bit stream. This process is done to ensure that the sender sends the data which is meaningful to the receiver.
In the same way, neural networks cluster the input in different ways and then classify it to provide the required output. The neural network groups the data as per its similarities and then classifies the same after receiving a labelled dataset that they can train on.
Neural Network Elements
A neural network is made up of multiple units connected which are called nodes. The nodes are the tiniest part of the network which works in the same way as a neuron behaves in a human brain.
When this neuron gets a signal, it will trigger a process. This signal will be then passed to the next node depending on the received input. This complex structure then uses feedback to learn.
The nodes present in the deep neural network are then grouped into different layers. Here, the given task will be solved by processing different input and output layers.
Now, with the increase in the number of layers that you will have to process, the depth of the network will increase. This is where the term deep learning came from. To determine the number of layers you need to process, Credit Assignment Path is used. When the value of the CAP index becomes greater than two, the neural network is said to be deep.
The node will then combine the data from the input to a coefficient set which then dampens or amplify the input. This adds importance to the input in the context of the specific task that the algorithm is attempting to learn.
After this, the products of input weights are summed and this sum is then passed through the activation function of the node. This function then determines the extent to which the signal must progress further to make an impact on the outcome.
In case the signal is passed, you can say that the neuron is passed. There is also a node layer present which is a row of switches. These switches are turned on and off when the input is being fed through the network.
The output generated at one level becomes an input for the next level beginning from the initial layer.
How to Use Greedy Layer Wise Training In Deep Neural Networks?
Before understanding the greedy layer-wise training in deep networks, let’s go through the basics. When we are training any neural network, we have to make a propagation in the forward direction and then calculate the total cost of the network. This cost is later used to propagate in the backward directions and update the input weights. The whole process is followed until you obtain the global minima.
However, the issue here is that these traditional techniques are only suitable for small networks because they may lead to the vanishing gradient problem.
Therefore, to overcome the vanishing gradient problem, greedy layer-wise training in deep networks is used.
Steps included in greedy layer-wise training
- To begin with, we will have to create a base model and then train that base model based on the available dataset.
- When the training is done, we will have to shift the output layer to another variable.
- Now, simply add a new hidden layer to the model which will be the first layer now and also, add the output layer back to the model.
- You will now have to train the model along with the hidden layer.
- After completing the training, if you have to add a new hidden layer, again shift the output layer to another variable and make sure to set all the elements as non-trainable.
- You will now have to add the next hidden layer and then add the output layer again.
- Again, train the model.
- You will have to repeat the same set of steps for all the layers you wish to add.
Conclusion
Upskilling to the latest and in-demand technologies is required to thrive in the industry and there is no doubt that neural networks are one of those technologies.
By the end of this post, we are certain that you have got an idea of what deep neural networks are, how they work and why greedy layer-wise training of deep networks is performed.
By Akshay Parashar
– A professionally trained Tech Expert, with great experience in Data Science, SQL, Machine Learning, Python, and Deep Learning.
Member since December, 2022
View all the articles of Akshay Parashar.
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