Deep Learning Algorithm [Comprehensive Guide With Examples]

Deep Learning Algorithm [Comprehensive Guide With Examples]

Here I will guide deep learning algorithms

Introduction

Deep Learning is a subset of machine learning, which involves algorithms inspired by the arrangement and functioning of the brain. As neurons from human brains transmit information and help in learning from the reactors in our body, similarly the deep learning algorithms run through various layers of neural networks algorithms and learn from their reactions.

In other words, Deep learning utilizes layers of neural network algorithms to discover more significant level data dependent on raw input data. The neural network algorithms discover the data patterns through a process that simulates in a manner of how a human brain works.

Neural networks help in clustering the data points from a large set of data points based upon the similarities of the features. These systems are known as Artificial Neural Networks.

As more and more data were fed to the models, deep learning algorithms proved out to be more productive and provide better results than the rest of the algorithms. Deep Learning algorithms are used for various problems like image recognition, speech recognition, fraud detection, computer vision etc.

Components of Neural Network

1. Network Topology – Network Topology refers to the structure of the neural network. It includes the number of hidden layers in the network, number of neurons in each layer including the input and output layer etc.

2. Input Layer – Input Layer is the entry point of the neural network. The number of neurons in the input layer should be equal to the number of attributes in the input data.

3. Output Layer – Output Layer is the exit point of the neural network. The number of neurons in the output layer should be equal to the number of classes in the target variable (For classification problem). For regression problem, the number of neurons in the output layer will be 1 as the output would be a numeric variable.

4. Activation functions – Activation functions are mathematical equations that are applies to the sum of weighted inputs of a neuron. It helps in determining whether the neuron should be triggered or not. There are many activation functions like sigmoid function, Rectified Linear Unit (ReLU) , Leaky ReLU, Hyperbolic Tangent, Softmax function etc.

5. Weights – Every interconnection between the neurons in the consecutive layers have a weight associated to it. It indicates the significance of the connection between the neurons in discovering some data pattern which helps in predicting the outcome of the neural network. Higher the values of weight, higher the significance. It is one of the parameters that the network learns during its training phase.

6. Biases – Bias helps in shifting the activation function to the left or right which can be critical for better decision making. Its role is analogous to the role of an intercept in the linear equation. Weights can increase the steepness of the activation function i.e. indicates how fast the activation function will trigger whereas bias is used to delay the triggering of the activation function. It is the second parameter that the network learns during its training phase.

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