# Tag: Algorithm

### Role of Convolutional Layer in Convolutional Neural Networks

The indispensable feature of convolutional neural networks, also denoted as CNNs, is the convolutional layer, which is commonly perceived as the pivotal and fundamental component that confers CNNs their appellation. The current layer is engaged in conducting a computational procedure commonly known as a “convolution”. After the successful initialization of all variables and assigning them with random values, the image of a deer is utilized with specific parameters..

### Top Machine Learning Algorithms – Data Scientist Basic Tool Kit

Learning Machine Learning skills is widely seen as a game-changing advantage for organizations, especially those with data-driven operations, as it has the potential to provide significant benefits. Nowadays, the most common term used to describe digital communication tools is social media platforms. The main aim of this written communication is to explicate and exemplify the prominent machine learning algorithms.

### Deep Learning – Backpropagation Algorithm Basics

Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent concerning weights using the backpropagation algorithm. This algorithm is actually based on the linear algebraic operation with a goal of optimising error function by harnessing its intelligence and provisioning updates.

### Machine Learning – Introduction to Reinforcement Learning

Reinforcement learning is closely related to dynamic programming approaches to Markov decision processes (MDP). MDP solve a partially observable problem. POMDPs received a lot of attention in the reinforcement learning community. As its a process of discrete-time stochastic control to provide a mathematical framework for decision-making modelling.

### Naive Bayes Algorithm – The basics you need to know

The naive Bayes algorithm is a method set of probabilities. For each attribute from each class set, it uses probability to make predictions. This algorithm falls under a supervised machine-learning approach. The data model that comes out of this effort is called as “Predictive Model” with probabilistic problems at the foundation level

### How Machine Learning Algorithms Works: An Overview

Machine learning employs computational techniques that empower algorithms to extract valuable insights from data, unconstrained by predefined equations. Through statistical analyses, ML algorithms unravel meaningful patterns and relationships within the data. These discoveries subsequently facilitate automated predictions and decisions, eliminating the need for explicit programming instructions.

### Unsupervised Learning an Angle for Unlabelled Data World

System does self-discovery of patterns, regularities and features etc. from the input data and relations for the input data over output data. Discovering similarities and dissimilarities to forms clusters i.e. self-discovery is main target here. Since the examples given to the learner are unlabeled

### Supervised Machine Learning – Insider Scoop for labelled data

Machine learning algorithms “learns” from the observations. When exposed to more observations, the algorithm improves its predictive performance. What’s going to happen to the stock market tomorrow? Is a task of deducing function from labeled training data.

### Data Science of Payments

How come my bank knows what I am going to buy next, how come my internet browser offering me add on something which I was searching on google few minutes or days backs. How  do they know my voice or can recognize my picture without any human intervention. Answer is much simpler then it looks or simpler then  the complexity of out own thought process. Use of deep learning

### The Science of Credit: Deciphering & Building The Credit Algorithms

Credit algorithms are really important in deciding how much money we can get or how much we can spend. Complicated math models are used to figure out credit scores and decide who can borrow money. Knowing how credit scoring works can help people to manage their money better and make smart choices.