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Machine Learning (ML)

Machine Learning is the fastest growing and most potential field that enables a computer to perform specific tasks better than humans. It is actively used in companies like Apple, Tesla, Google and Facebook. We are covering the latest developments in the field

Machine Learning (ML)

Interview Questions on Autoencoders

In this article, we have presented most important Interview Questions on Autoencoders.

Natural Language Processing (NLP)

NLP Project: Compare Text Summarization Models

In this article, we will go over the basics of Text Summarization, the different approaches to generating automatic summaries, some of the real world applications of Text Summarization, and finally, we will compare various Text Summarization models with the help of ROUGE.

Interview Questions on Transformers

The Transformers architecture introduced in the paper “Attention Is All You Need”, has changed the scenario of creating more complex and advanced NLP models. Following are the important questions for an interview on Transformers.

Machine Learning (ML)

PCA vs LDA [Differences]

This problem charged us much time, memory spaces and make difficult for our models to work with until PCA and LDA was born. In this article, we're going to discuss about how these 2 algorithms work and the differences between them.

List of Interview Questions

Interview questions on GAN

In this article, we have presented several Interview questions on Generative Adversarial Network (GAN) along with detailed answers.

Machine Learning (ML)

Huber Fitting using ADMM

Huber Fitting in general is the approach of using the Huber function to fit the data models, the advantage of this approach is due to the clever formulation of the Huber function which brilliantly combines the best features of both preceding optimization solution approaches of LAD and LS.

Machine Learning (ML)

LASSO using ADMM

LASSO is the acronym for Least Absolute Shrinkage and Selection Operator. Regression models' predictability and interpretability were enhanced with the introduction of Lasso.

Machine Learning (ML)

Least Absolute Deviation using ADMM

Least Absolute Deviation (LAD) is a powerful approach for solving optimization problems with good tolerance to outliers. Hence solving it to obtain a practicably applicable form is essential to take advantage of its theoretical prowess.

Machine Learning (ML)

Sentiment Analysis with Naive Bayes Classifier Built from Scratch

In this article, we will implement a Naive Bayes classifier from scratch to perform sentiment analysis.

Machine Learning (ML)

ADMM - Alternating Direction Method of Multipliers

Distributed convex optimization, and in particular, large-scale issues occurring in statistics, machine learning, and related fields, are particularly suited to the Alternating Direction Method of Multipliers (ADMM).

Machine Learning (ML)

Drone simulation with object detection

Drones are Unmanned Aerial Vehicles (UAV) that are remotely controlled either by humans or by computer programs. They range in size from under one pound to several hundred pounds.

Machine Learning (ML)

Panoptic Segmentation

Panoptic Segmentation is an improved human-like image processing approach that combines the goals of both Instance and Semantic Segmentation. It was first proposed in a 2018 paper by Alexander Kirillov.

Machine Learning (ML)

Confusion matrix and how accuracy is not the key always

Confusion matrix is a term in the field of machine learning which is generally associated with a lot of confusion on what it means, which is exactly what will be removed in this article as we understand confusion matrix.

Computer Architecture

GCC Compiler Intrinsics

In this article, we will discuss the GNU Compiler Collection (GCC), the fundamentals of intrinsics, some of the ways in which these intrinsics can speed up vector code, and we will also take a look at a list of some of the x86 intrinsics that GCC offers.

Machine Learning (ML)

Saving and Reusing Machine Learning Models

In this article, we will be learning about various ways of saving and reusing machine learning models.

Machine Learning (ML)

Types of Generative Adversarial Networks (GANs)

In this article, we cover the types of GAN's. A Generative Adversarial Network is a machine learning algorithm that is capable of generating new training datasets.

Machine Learning (ML)

Beginner's Guide to Google Colaboratory

In this article, we will be learning about Google Colaboratory, an excellent tool for data scientists.

Computer Architecture

SIMD & SSE Instruction Set

In this article, we will discuss scalar computing (and some of its drawbacks), the need for vector/parallel computing, the fundamental concepts behind single instruction, multiple data (or SIMD) architecture and SSE.

AO* algorithm

AO* algorithm is a best first search algorithm. AO* algorithm uses the concept of AND-OR graphs to decompose any complex problem given into smaller set of problems which are further solved.

Machine Learning (ML)

Contrastive Learning

Contrastive learning is a method for structuring the work of locating similarities and differences for an ML model. This method can be used to train a machine learning model to distinguish between similar and different photos.

AVX-512

In this article, we will discuss Intel's Advanced Vector Extensions 512 (AVX-512), which is an instruction set that was created to accelerate computational performance in areas such as artificial intelligence/deep learning.

F Test

F-tests get their name from the F test statistic, which was named after Sir Ronald Fisher. The F-statistic is just a two-variance ratio. Variances are a metric for dispersion, or how far the data deviates from the mean. Greater dispersion is shown by higher values.

Machine Learning (ML)

Posenet Model in ML

In this article, we will learn about pre-trained model PoseNet in detail which will be consisting of need and working of posenet, operations possible on it, its application, and possible improvement over existing posenet model.

Machine Learning (ML)

Transposed Convolution

Transposed convolution is also known as upsampled convolution, which refers to the task it accomplishes, which is to upsample the input feature map.

Machine Learning (ML)

Grouped and Shuffled Grouped Convolution

In this article, we have explored the variant of Convolution named Grouped and Shuffled Grouped Convolution.