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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

Natural Language Processing (NLP)

Stop Words in NLP

In this article, we shall focus on the concept of stopwords and implementation of stopword removal in NLP.

Machine Learning (ML)

Large Counts Condition and Large Enough Sample Rule

Large Counts Condition and Large Enough Sample Rule are two important concepts in the fields of machine learning and statistics that are used to make inferences about populations based on samples.

Natural Language Processing (NLP)

POS Tagging in NLP using Python

POS tagging is a text preprocessing task within the ambit of Natural Language Processing (NLP) whose goal is to analyze the syntactic structure of a given sentence and to understand the input text in a better manner.

Machine Learning (ML)

Benford's Law in ML

In this article, we have explored Benford's Law and the use of it in the field of Machine Learning. This is one of the core ML laws you must master.

Layer Normalization: An Essential Technique for Deep Learning Beginners

Layer normalization is a relatively new technique in the field of deep learning. It was first introduced by Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey Hinton in their 2016 paper "Layer Normalization".

Tic Tac Toe with Reinforcement Learning

Tic Tac Toe is one of the most popular game which needs only two players to play it. This classic game is developed with almost every well-known programming language. In this article, the game is developed using Reinforcement Learning.

Machine Learning (ML)

Association in Unsupervised Learning

An overview of Association and it's implementation using unsupervised learning methods in Python.

Biomedical Image Segmentation

In the Biomedical field segmented images can be used for anomaly detection, diagnosing diseases, computer-integrated surgery, treatment planning, studying anatomical structures, and much more.

Multi-Layer Perceptron (MLP): A Basic Understanding

Multi Layer Perceptron (MLP) is a type of artificial neural network that is widely used for various machine learning tasks such as classification and regression. It is called a multi-layered perceptron because it has many layers of nodes (known as artificial neurons) that connect to each other.

Machine Learning (ML)

30+ Computer Vision Projects

In this article, we will explore over 30 Computer Vision (CV) projects that will help boost your portfolio. We will discuss in brief each project along with the models used, datasets used, project domain, codebase and research paper.

Techniques to detect Deepfake videos

Hello everyone, Today we are going to discuss one of the most morally sensitive subject in Artificial Intelligence world which are DeepFake and how we try to detect the deep fake from real.

Self-attention in Transformer

Today we will discuss one of the revolutionary concepts in the artificial intelligence sector not only in Natural Language Processing but also nowadays in the Computer Vision, which is the Transformers and the heart of it Self-Attention.

2D Convolution in Python

In this article, we will understand the concept of 2D Convolution and implement it using different approaches in Python Programming Language.

Single Image Super Resolution

We will explain to you about a very important and strong application in image processing and of course in Machine Learning which is "Single Image Super Resolution". We have listed different model architectures, datasets and research papers for this application (Single Image Super Resolution).

Calibration in Machine and Deep Learning

In this article, I introduce calibration in Machine Learning and Deep Learning, an useful concept that not many people know.

Instance Segmentation

In this article, we will dive deeper in a very important concept in computer vision which considered a great progress in deep learning field which is Instance Segmentation. what is it?, the most popular algorithm for it, and its evaluation metric.

Machine Learning (ML)

Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ)

In this article, we will dive deeper in evaluation metrics for computer vision tasks especially for Panoptic segmentation namely Panoptic quality (PQ), segmentation quality (SQ) and recognition quality (RQ).

Fault Detection System: Predict defective solar module cells

In this article we will build a fault detection system using a deep learning model : EfficientNet to distinguish between defective and non-defective cells that are extracted from solar modules.

Perlin Noise (with implementation in Python)

One of the most important algorithms in computer graphics and procedural generation is Perlin Noise. Perlin Noise is an algorithm that generates textures and terrain-like images procedurally (without the need for an artist to manually create the images).

Wake Sleep Algorithm For Neural Network

In this article, we have explored the Wake Sleep Algorithm For Neural Network along with examples of using it and limitations of it.

Types of Gradient Optimizers in Deep Learning

In this article, we will explore the concept of Gradient optimization and the different types of Gradient Optimizers present in Deep Learning such as Mini-batch Gradient Descent Optimizer.

VGG54 and VGG22

VGG54 and VGG22 are loss metrics to compare high and low resolution images by considering the feature maps generated by VGG19 neural network model.

Gradient Accumulation [+ code in PyTorch]

Gradient Accumulation is an optimization technique that is used for training large Neural Networks on GPU and help reduce memory requirements and resolve Out-of-Memory OOM errors while training. We have explained the concept along with Pytorch code.

Machine Learning (ML)

Evaluation metrics for object detection and segmentation

In this article, we will go through the Evaluation metrics for Object Detection and Segmentation that is Image segmentation, semantic segmentation and instance segmentation in depth.

Calculate mean and std of Image Dataset

In this article, we have explained how to calculate the mean and standard deviation (std) of an image dataset which can be used to normalize images in the dataset for effective training of Neural Networks.