×

Home Discussions Write at Opengenus IQ

×

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)

Linear Discriminant Analysis (LDA)

The process of predicting a qualitative variable based on input variables/predictors is known as classification and Linear Discriminant Analysis (LDA) is one of the techniques, or classifiers. We have explained the inner workings of LDA for dimensionality reduction.

Connected Component Labeling

This article covers: Connected Components (also known as Connected Component Analysis, Blob Extraction, Region Labeling, Blob Discovery or Region Extraction), Connected Component Labeling, Algorithms and an Example of Applying in Python using OpenCV.

Machine Learning (ML)

What is Linear Regression?

Linear Regression is regression technique modelling relationship between dependent variable(y) and one or more independent variables(x) by using a linear approach.

Machine Learning (ML)

Practical Black box Attacks against Machine Learning

There are several techniques which can be used to fool any Machine Learning model without having any information regarding the model like model architecture or training dataset. We have explored an influential research regarding this topic.

Machine Learning (ML)

MaxPool vs AvgPool

In this article, we have explored the difference between MaxPool and AvgPool operations (in ML models) in depth. In short, in AvgPool, the average presence of features is highlighted while in MaxPool, specific features are highlighted irrespective of location.

Machine Learning (ML)

Purpose of different layers in a Deep Learning Model

In this article, we have explored the significance or the importance of each layer in a Machine Learning model. Different layers include convolution, pooling, normalization and much more. For example: the significance of MaxPool is that it decreases sensitivity to the location of features.

Machine Learning (ML)

Contrast Enhancement Algorithms

This article covers: Contrast and Contrast Enhancement, Types of Contrast Enhancement: Histogram Equalization, Contrastive Limited Adaptive Equalization, Contrast Stretching and how to implement these in Python using OpenCV.

Machine Learning (ML)

Different Word Representations

We have discussed the different word representations such as distributional representation, clustering based representation and distributed representation with several sub-types for each representation.

Machine Learning (ML)

Calculate output size of Convolution

In this article, we have illustrated how to calculate the size of output in a convolution provided we have the dimensions of input data, kernel, stride and padding.

Machine Learning (ML)

Hidden Layers

Hidden layers are one of the most important parts of Neural Networks. The input layer contains input neurons that send information to the hidden layer. The hidden layer sends data to the output layer.

Machine Learning (ML)

Understanding Neural Networks Through Deep Visualization by Jason Yosinski

This article explains the advanced techniques proposed by Jason Yosinski to understand the hidden working pattern of Neural Networks in the paper "Understanding Neural Networks Through Deep Visualization".

Machine Learning (ML)

Adversarial examples in the Physical world

In this article, we have explored a paper “Adversarial examples in the Physical world” by Alexey Kurakin, Ian J. Goodfellow and Samy Bengio which presents such examples and methods to create such examples.

Machine Learning (ML)

Bayesian Belief Networks

This article covers: Prerequisite Probability concepts for Bayesian Belief Networks(BBNs), BBN components: Directed Acyclic Graph and Conditional Probability Table and implementation example in Python.

Machine Learning (ML)

Explaining and Harnessing Adversarial examples by Ian Goodfellow

The article explains the conference paper titled "EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES" by Ian J. Goodfellow et al in a simplified and self understandable manner.

Machine Learning (ML)

Understanding ResNet50 architecture

ResNet50 is a variant of ResNet model which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer. It has 3.8 x 10^9 Floating points operations. It is a widely used ResNet model.

Machine Learning (ML)

Value Iteration Algorithm (with a 1D example)

In this article, we have explored Value Iteration Algorithm in depth with a 1D example. This algorithm finds the optimal value function and in turn, finds the optimal policy.

Machine Learning (ML)

Applications of Principal Component Analysis (PCA)

This article gives a brief introduction to PCA and explains the applications of Principal Component Analysis in Neuroscience, Quantitative Finance, Image Compression (with a coding example), Facial Recogntion and others.

Machine Learning (ML)

One Pixel Attack for Fooling Deep Neural Networks

In this article, we have explored the paper "One Pixel Attack for Fooling Deep Neural Networks" by Jiawei Su and others which introduces a technique to modify only one pixel in an image which will lead popular models to mislabel them.

Machine Learning (ML)

Machine Learning Approach for Sentiment Analysis

The Lexical methods of Sentiment Analysis, even though easy to understand and implement, are not proven to be very accurate. Thus, we discuss the Machine Learning approach for Sentiment Analysis, focusing on using Convolutional Neural Networks for Sentiment Analysis.

Machine Learning (ML)

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

In this article, we will have a simplified view of the research paper "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images" by Anh

Machine Learning (ML)

Performance metrics in Classification and Regression

In this post, we will look at different performance metrics for classification and regression. For regression, metrics are Mean absolute error, Mean squared error, R-Square and many more. For classification, the metrics are accuracy, precision, recall and many more.

Machine Learning (ML)

Hindi OCR (Optical Character Recognition)

Hindi OCR is basically a model which is used to recognize handwritten Hindi (Devanagari) characters. We have demonstrated this with a custom CNN model.

Machine Learning (ML)

Understand AdaBoost and Implement it Effectively

In this article we will see how AdaBoost works and we will see main advantages and disadvantages that lead to an effective usage of the AdaBoost algorithm.

Machine Learning (ML)

Lexicon based Sentiment Analysis

Lexicon-based Sentiment Analysis techniques, as opposed to the Machine Learning techniques, are based on calculation of polarity scores given to positive and negative words in a document.

Machine Learning (ML)

Simplifying "Intriguing properties of neural networks"

In this article, we have explored the paper "Intriguing properties of neural networks" by Christian Szegedy in depth as it is an influential paper which introduces two key properties that define Neural Networks.