Neural Networks Demystified: A Beginner's Guide to Understanding the Fundamental of Neural Network

Neural Networks Demystified: A Beginner's Guide to Understanding the Fundamental of Neural Network
6 min read

For a few years now, artificial intelligence (AI) has been a prominent term in the technological industry. It is altering the way we live, work, and interact with our surroundings. Neural networks, which are designed after the structure of the human brain, are an important component of AI. In this blog, we'll go over the fundamental of neural network, such as how they function, their applications, and the difficulties that come with developing them.

Fundamental of Neural Network

Neurons, which are patterned like neurons in the human brain, are at the center of any neural network. Each neuron takes information from other neurons or from outside sources, analyzes that input, and then creates an output. The output of one neuron may then be utilized as the input for another, allowing data to flow across the network.

Neurons are often structured into layers, with each layer carrying out a certain sort of processing. External sources, such as sensors or other devices, provide input to the input layer. The output layer generates the network's ultimate output, such as a prediction or judgment.

One or more hidden layers provide intermediary processing between the input and output layers. Each neuron in a layer is linked to every neuron in the layer before it, and the strength of those connections may be modified during training to increase network performance.

The role of activation functions

The activation function is a basic computation performed by each neuron in a neural network based on its inputs. The activation function decides whether the neuron "fires" or if its output is passed on to the next layer.

There are various sorts of activation functions, each with its own set of benefits and drawbacks. The sigmoid function, which provides an output between 0 and 1, is the most frequent activation function. The rectified linear unit (ReLU) function and the hyperbolic tangent function are two more common activation functions.

Types of neural networks

There are various varieties of neural networks, each suited to a certain sort of task. The most prevalent kinds are as follows:

  • Feedforward neural networks: This is the most basic sort of neural network, with information flowing from input to output in only one direction. Feedforward networks are extensively used for classification and prediction applications.
  • Convolutional neural networks: This sort of neural network was created primarily for image and video processing jobs. Convolutional neural networks can recognize picture properties such as edges and corners and utilize those features to categorize images.
  • Recurrent neural networks: Recurrent neural networks are intended for applications involving sequential data, such as time series analysis or language processing. Recurrent networks may "remember" prior inputs and utilize that knowledge to influence their outputs.

Training neural networks

Training a neural network entails altering the strength of neural connections in order to increase the network's performance. This is often accomplished through the use of a process known as backpropagation.

Backpropagation works by first feeding input data into the network and then comparing the output of the network to the proper output. The difference between the two outputs is then utilized to compute an error value, which is subsequently transmitted back through the network. The error value is used to alter the strength of neural connections in order to improve network performance.

There are several training approaches, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning includes labeled data is used to train the network, whereas unsupervised learning involves no labeled data being used to train the network. Reinforcement learning entails training the network with a reward and punishment scheme.

Deep Learning

Deep learning is a form of neural network with numerous processing layers. Deep networks may learn more complicated characteristics than shallow networks and can execute tasks that were previously thought to be impossible.

Deep learning has been employed in a variety of applications, including picture and audio recognition, natural language processing, and self-driving cars. The deep convolutional neural network (CNN), which is often used for image and video processing applications, is one of the most prominent deep learning designs.

CNNs employ a method known as convolution, which entails sliding a tiny filter over an image and completing a basic computation at each spot. The result of the convolution process is then sent into the next layer after being passed via an activation function.

Recurrent neural networks

Recurrent neural networks (RNNs) are intended for applications involving sequential data, such as time series analysis or language processing. RNNs may "remember" prior inputs and utilize that knowledge to inform their outputs.

The long short-term memory (LSTM) network is a prominent RNN design. LSTMs manage the flow of information across the network using a gate mechanism, allowing them to learn long-term dependencies from sequential input.

Language translation, speech recognition, and handwriting recognition are all applications of RNNs. RNNs have also been employed in the creation of music and video analysis.

Common Challenges in Neural Networks

Despite their outstanding performance, neural networks are not without difficulties. Among the most prevalent difficulties are:

  • Overfitting: This occurs when the network gets too specialized for the training data and performs badly on fresh data.
  • Underfitting: This happens when the network is too simplistic to learn data patterns and performs badly on both training and fresh data.
  • Vanishing gradient problem: as the gradient used to modify the connections between neurons becomes too modest to be useful, the learning process is slowed.
  • Exploding gradient problem: When the gradient grows too large, the network becomes unstable and fails to learn.

To address these issues, academics have devised a number of approaches, including regularization and dropout.

Vidhi Yadav 19
Joined: 9 months ago
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