The cumulative hazard plot is a graphical representation that helps us understand the reliability of a model fitted to a given dataset. Specifically, it provides insights into the expected time of failure for the model. The cumulative hazard function for the Weibull distribution describes the accumulated risk of failure up ... Read More

PointNet analyzes point clouds by directly consuming the raw data without voxelization or other preprocessing steps. A Stanford University researcher proposed this novel architecture in 2016 for classifying and segmenting 3D representations of images. Key Properties Within point clouds, PointNet considers several key properties of Point Sets. A Point Cloud ... Read More

Introduction The idea of filter groups, also known as grouped convolution, was first explored by AlexNet in 2012. This creative solution was prompted by the necessity to train the network using two Nvidia GTX 580 GPUs with 1.5GB of memory each. Challenge: Limited GPU Memory During testing, AlexNet's creators discovered ... Read More

Introduction Have you ever wondered how humans are able to perceive and understand the visual world with limited sensory inputs? It's a remarkable ability that allows us to compose complex visual concepts from basic elements. In the field of computer vision, scientists have been trying to mimic this compositional behavior ... Read More

The linear regression method compares one or more independent variables with a dependent variable. It will allow you to see how changes in the independent variables affect the dependent variables. A comprehensive Python module, Statsmodels, provides a full range of statistical modelling capabilities, including linear regression. Here, we'll look at how ... Read More

Introduction LSTM, which stands for Long Short-Term Memory, is an advanced form of recurrent neural network (RNN) specifically designed to analyze sequential data like text, speech, and time series. Unlike conventional RNNs, which struggle to capture long-term dependencies in data, LSTMs excel in understanding and predicting patterns within sequences. Conventional ... Read More

Introduction In recent years, deep neural networks (DNN) have made significant progress in reinforcement learning algorithms. In order to achieve desirable results, these algorithms, however, suffer from sample inefficiency. A promising approach to tackling this challenge is episodic memory-based reinforcement learning, which enables agents to grasp optimal actions rapidly. Using ... Read More

Introduction The success of machine learning algorithms depends on the quality of the data they use to extract knowledge. Machine learning algorithms may produce inaccurate or unintelligible results if data is inadequate or contains irrelevant information. By removing irrelevant and redundant information before learning, feature subset selection algorithms aim to ... Read More

Introduction Spectral analysis plays a crucial role in understanding and modeling sinusoidal components in various fields such as statistics, signal processing, and time series analysis. Sinusoidal models are widely used to approximate sequences of data by fitting them to sine functions In this blog, you will be able to understand ... Read More

Autoencoder networks, which are also referred to as auto-associative neural networks, are a specific type of neural network that is really good at replicating input patterns at the output layer and they can be achieved significant accomplishments in various domains, such as identifying patterns, analyzing biological information, recognizing speech, and ... Read More