Murugesh Manthiramoorthi - OpenGenus IQ: Learn Algorithms, DL, System Design

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)

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)

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)

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)

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.

Machine Learning (ML)

Simplifying "Gaussian LDA for Topic Models with Word Embeddings"

In this article, we have explained the research paper titled "Gaussian LDA for Topic Models with Word Embeddings" by Rajarshi Das, Manzil Zaheer, Chris Dyer (associated with Carnegie Mellon University) in a easy fashion for beginners to get deep understanding of this paper

Machine Learning (ML)

Pachinko Allocation Model (PAM)

Pachinko Allocation Model (PAM) is a topic modeling technique which is an improvement over the shortcomings of Latent Dirichlet Allocation. In this article, we have explained it in detail.

Machine Learning (ML)

Topic Modeling using Non Negative Matrix Factorization (NMF)

Non-Negative Matrix Factorization is a statistical method to reduce the dimension of the input corpora. It uses factor analysis method to provide comparatively less weightage to the words with less coherence.

Machine Learning (ML)

Topic Modeling using Latent Semantic Analysis

In this article, we have explored the functioning and working of Latent Semantic Analysis with respect to topic modeling in DEPTH along with mathematics behind the method.

Machine Learning (ML)

Topic Modelling Techniques in NLP

Topic modelling is an algorithm for extracting the topic or topics for a collection of documents. We explored different techniques like LDA, NMF, LSA, PLDA and PAM.

Machine Learning (ML)

Demystifying Voting Classifier

Voting classifier is one of the most powerful methods of ensemble methods which we have explored in depth in this article.