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Articles by Sohail Tabrez
Page 3 of 6
What is Propositional Logic Based Agent?
Introduction An agent learns to make decisions by interacting with its surroundings in a type of machine learning known as reinforcement learning. By getting feedback for its activities in the form of incentives or penalties, the agent learns. Robotics, video games, and self-driving cars are just a few examples of the many applications for reinforcement learning. We will thoroughly examine the theories and methods underlying reinforcement learning in this article. Propositional Logic based Agent: A Comprehensive Overview Throughout the last few decades, the field of artificial intelligence (AI) has experienced significant advancement. Scientists and researchers are developing a variety of ...
Read MoreSelection of GAN vs Adversarial Autoencoder models
Introduction For the past few years, generative models have attracted a lot of attention in the deep learning community. Among these, Adversarial Autoencoders (AAEs) and Generative Adversarial Networks (GANs) are two of the most well-liked models for producing realistic images. While AAEs are more adapted to producing various images that accurately capture the core of the training data, GANs are better suited to producing high-quality images that closely resemble the training data. We will talk about choosing GAN and AAE models for problems involving image generation in this article. Generative Adversarial Network (GAN) Ian Goodfellow introduced generative adversarial networks (GANs) ...
Read MoreUnderstanding Reinforcement Learning in-depth
Introduction An agent learns to make decisions by interacting with its surroundings in a type of machine learning known as reinforcement learning. By getting feedback for its activities in the form of incentives or penalties, the agent learns. Robotics, video games, and self-driving cars are just a few examples of the many applications for reinforcement learning. We will thoroughly examine the theories and methods underlying reinforcement learning in this article. Reinforcement Learning A subset of machine learning called reinforcement learning emphasizes learning via feedback. The interaction between an agent and its environment is used to model the learning process. By ...
Read MoreML Applications by Google
Introduction Machine learning has been a hot topic in the IT industry for the past ten years due to Google's continued dominance in its development and application. From improving search engine results to developing self-driving cars, Google has been leveraging the power of ML to address difficult problems and enhance user experiences. In this article, we'll take a closer look at some of Google's best machine learning (ML) products and how they affect our daily lives. What are the Machine Learning Applications by Google? Google Search One of the most widely used online tools is Google's search engine, and machine ...
Read MoreImplementation of a CNN based Image Classifier using PyTorch.
Introduction Due to its capacity to recognise spatial characteristics in images, convolutional neural networks (CNNs) have been extensively used in image classification applications. A well-liked open-source machine learning package called PyTorch offers assistance in creating and honing neural networks, including CNNs. In this article, we'll go over how to use PyTorch to create a CNN-based image classifier. Dataset Let's first talk about the dataset before getting into the specifics of the implementation. The CIFAR-10 dataset, which has 60, 000 32x32 color images divided into 10 classes with 6, 000 images each, will be the one we use for this course. ...
Read MoreRelationship between ML and the Scientific Method
Introduction In several industries, including healthcare, banking, transportation, and others, machine learning (ML) has become a potent tool for addressing a variety of issues. The scientific method, however, has been the foundation of scientific investigation for generations. ML and the scientific method have a close association despite the two appearing to be extremely distinct from one another. This article will explore this connection and look at how the two can cooperate to further scientific understanding. What is The Scientific Method? The scientific method is a methodical way to look into occurrences and produce new knowledge. There are several steps involved, ...
Read MoreExamples of Machine Learning based Mobile Applications
Introduction Mobile applications have undergone a revolution, thanks to machine learning, becoming smarter, more individualised, and more effective. There are several ways that machine learning is used in mobile apps, from audio and image recognition to natural language processing and predictive analytics. Today's world is undergoing rapid change, and technology is developing at an unheard-of rate every day. Machine learning, a subset of artificial intelligence that really has fundamentally changed how we use technology in our daily lives, has been one of the most fundamental technological advancements in recent years. Mobile applications are a area wherein machine learning has significantly ...
Read MoreDistribution of Test Data vs. Distribution of Training Data
Introduction The quality and representativeness of the data used to train and test a machine learning model significantly impact its success. The distribution of training and test data is a key factor in determining the quality of the data. The distribution of training data is the probability distribution of the input data used to train a machine learning model. In contrast, the probability distribution of the input data used to assess the model's effectiveness is referred to as the distribution of test data. This article will examine the variations in training and test data distributions and how they may affect ...
Read MoreLogistic Regression with Two Highly Correlated Predictors
Introduction Logistic Regression is a widely used statistical technique applied in various fields to model the relationship between a binary response variable and a set of predictor variables. This technique is an extension of linear Regression, where the dependent variable is transformed to a logit function to ensure that the predictions lie within the range of 0 and 1. In this article, we will discuss the implications of having two highly correlated predictors in a logistic regression model and the steps that can be taken to address this issue. Logistic Regression: Dealing with Highly Correlated Predictors Correlation among predictors in ...
Read MoreAssumptions of Linear Regression - Multivariate Normality
Introduction Linear regression is a widely used statistical method for modelling the relationship between a dependent variable and one or more independent variables. It is based on the linear relationship between the variables and is widely used in various fields, including economics, psychology, and engineering. However, certain assumptions must be met for the results of linear regression analysis to be meaningful and accurate. One of these assumptions is the assumption of multivariate normality. Multivariate normality assumes that the residuals, or the difference between the observed and predicted values, are normally distributed. This assumption is important because it allows for various ...
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