Overview
Deep learning is the main key component to dealing with large and complex datasets. It has achieved many successes in various domains. Still, it also holds some challenges such as the need for labeled data at a large scale, computational resources, and potential issues with interpretability. Researchers are on best practices to continue to address these challenges and explore new innovative techniques.
What’s Next!
In this comprehensive guide to Neural Networks, we will highlight the concept of Neural Networks and explore what a neural network is, its key components, and other related terms.
Table of Contents
What is a Neural Network?
A Neural Network is based on the human brain’s structure and function. It is also known as artificial neural networks (ANNs). It is characterized as the layers which include three layers, input layer, hidden layer, and output layer. The layers are organized as interconnected Nodes.
Simply put, a Neural Network is defined as a set of algorithms based on the human brain’s functions to train the model. This is defined as Nodes in multiple layers. The Neural Network aims to mimic the human brain to perform the human task. In this context, Artificial Intelligence is the Neural Network’s root, enabling human-like thinking to perform tasks.
What are the main key components of a Neural Network?
Let’s take into consideration various kinds of Neural networks!
- Nodes: Nodes also known as Neurons, are the component of the Neural Network. Every Neuron receives input in various features and passes to the hidden layers which make some computations and pass to the output layer to produce the output. In this sense, the nodes are the main key to represent the input data to the meaningful desired output.
- Weight and Biases: The weight is referred to as the numerical value of the connection between the nodes. Each weight is defined as the strength of the nodes. Also, each neurons are internally connected with biases. During the training period, the weight and biases are adjusted to reduce the difference between the predicted and desired output.
- Activation Function: The activation function computes the output of the neurons based on the given input function. It allows the non-linearity to the network and allows it to learn more about the difficult and complex structure of the algorithms. Other Activation functions are sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU).
- Layers: There are three types of layers in Neurons.
Input Layer: This is the first layer in the neurons, and receives the data as input in the form of various features.
Hidden Layer: The hidden layer is the next layer after the Input layer, these layers read the input data in the form of assigned weighted connection and through the activation function, help to understand the computation method.
Output Layer: The output layer is the last in the neurons. It processes the data from the previous layers and returns the desired output.
5. Feedforward and Backpropagation:
Feedforward: In the training period, the input data is passed forward through the layers to compute the predictions.
Backpropagation: First, the model’s prediction and actual output are compared to find any error. This error is then processed in the back direction throughout the network adjusting the weights and biases through algorithms like gradient descent.
6. Learning: The ‘learning’ process of the Neural Network starts by adjusting the weight and biases in the training period. The neural network aims to predict the exact accurate result by minimizing the predicted and actual output, enabling the network over time to improve results on new unseen data.
What are the types of Neural Networks?
There are 3 types of Neural Networks. Let’s take them one by one!
- Feedforward Neural Networks (FNN): The simplest type of Neural Network is FNN, which transfers the data in one direction from the input to the output layer.
- Recurrent Neural Network (RNN): It directs the data sequentially. The connection is established to maintain the memory of the previous input data.
- Convolutional Neural Networks (CNN): This is a special type of neural network, used for image and visualization of data. By using the convolutional layers, it learns to spatial hierarchies of features.
How does Neural Network work?
The neural network is made of three components input, hidden, and output layer. The process begins with the input layer which is considered as the nodes and holds multiple features like f1, f2, and f3, etc. Features are nothing but the name to represent each node. Each node is associated with a weight value like w1, w2, and w3, etc. Next, each node feature and weight value is multiplied to find the summation.
The bias is the additional term added to the summation value. It provides the flexibility to the network to shift the decision boundary.
Now, the summation value is sent to the Hidden layer where the Activation Function (a = f (z)) finds the computation based on data from the input layer, and finally predicted result is sent to the output layer to compare the predicted and actual output to determine the exact output.
a = Where a is the final output produced after applying the Activation function.
f = f is the activation function.
z = z is the summation of input layer data.
In another words,
This process begins with calculating the summation after applying an activation function, the output is passed to the next layer and is repeated for each neuron in the network during the forward propagation stage. The weights and biases are adjusted during the training, through backpropagation and optimization algorithms to minimize the difference between the predicted and actual outputs.
Conclusion
Deep Learning is not the only concept to deal with complex large datasets but it holds a few terms inside itself such as Artificial Neural Networks (ANN), (FNN), (RNN), and (CNN). The neural network is the main component in Deep Learning to mimic the human brain functions and perform tasks as human does.
