Machine Learning Introduction

Corpus is the collection of our whole data X based on which our model will calculate to give out some data Y. When our model gives out some output Y based on the data stored in corpus as X is called Machine Learning, where we generally teach our model to give some output based on a lot of data fed by us. <h1>Deep Learning Model</h1>   In deep learning, we still have the concept of input data (X) and output (Y), similar to machine learning. However, deep learning models are characterized by their use of neural networks, which consist of multiple layers of interconnected nodes or neurons. These neurons are organized into layers, typically including an input layer, one or more hidden layers, and an output layer. The Input layer represents the initial data that you feed into the deep learning model. In the context of deep learning, this input data is often represented as a feature vector or a multidimensional array. The Hidden Layers are the intermediate layers between the input and output layers. They are called “hidden” because you typically don’t have direct access to their values. Each neuron in a hidden layer receives input from the previous layer and processes it through an activation function. Multiple hidden layers allow the model to learn increasingly complex representations of the data. The Output Layer produces the final output of the deep learning model. The format of the output depends on the specific task. For example, a classification task, it might produce probabilities for different classes, while in a regression task, it could produce a numerical value. <a href="https://medium.com/@aryanparashar24/machine-learning-introduction-7befb4d64ad5">Website</a>