Machine Learning (ML)

NASNet - A brief overview

NASNet stands for Neural Search Architecture (NAS) Network and is a Machine Learning model. The key principles are different from standard models like GoogleNet and is likely to bring a major breakthrough in AI soon.

The Idea of Indexing in NLP for Information Retrieval

We have explored the fundamental ideas for Information Retrieval that is Indexing Data. We have covered various types of indexes like Term document incidence matrix, Inverted index, boolean queries, dynamic and distributed indexing, distributed indexing and Dynamic Index.

Machine Learning (ML)

Applications of Random Forest

Random Forest is mainly used for classification tasks and is used widely in production (applications) like in credit card fraud detection, cardiovascular disease medicine and many more.

Machine Learning (ML)

Regression vs Correlation

We have explored the key differences between Correlation and Regression along with the basic idea behind both concepts. There are mainly 5 differences between Regression and Correlation.

Machine Learning (ML)

Wide and Deep Learning Model

Wide and Deep Learning Model is a ML/ DL model that has two main components: Memorizing component (Linear model) and a Generalizing component (Neural Network) and a cross product of the previous two components. Wide and Deep Learning Model is used in recommendation systems.

Machine Learning (ML)

RoBERTa: Robustly Optimized BERT pre-training Approach

RoBERTa (Robustly Optimized BERT pre-training Approach) is a NLP model and is the modified version (by Facebook) of the popular NLP model, BERT. It is more like an approach better train and optimize BERT (Bidirectional Encoder Representations from Transformers).

Machine Learning (ML)

Introduction to GPT models

Generative Pre-Training (GPT) models are trained on unlabeled dataset (which are available in abundance). There are different variants like GPT-1, GPT-2 and GPT-3 which we have explored.

Machine Learning (ML)

Filtering spam using Naive Bayes

Naive Bayes is a Bayes Theorem-based probabilistic algorithm used in data analytics for email spam filtering. In this article, we have explored the step to filter spam using Naive Bayes in depth.

Machine Learning (ML)

Disadvantages of GANs || Am I real or a Trained Model to write?

We have explored the problems with GANs in this article and have divided them into two major parts: General Problems with GANs and Technical Disadvantages of GANs.

Machine Learning (ML)

Elastic Net Regularization

Elastic Net Regularization is a regularization technique that uses both L1 and L2 regularizations to produce most optimized output. This is one of the best regularization technique.

Machine Learning (ML)

BERT and SEARCH: How BERT is used to improve searching?

In this article, we have explored how BERT model can be used to improve search results in search engines like Google Search, Bing and others.

Machine Learning (ML)

Introduction to Multilingual BERT (M-BERT)

We explored what is Multilingual BERT (M-BERT) and see a general introduction of this NLP model.

Machine Learning (ML)

Heaps' law in NLP for Frequency of Words

Heap's Law in NLP is a relation between the number of unique words to the total number of words in a document. It is, also, known as Herdan's law.

Machine Learning (ML)

Concrete Problems in AI Safety

In this article we shall explore a research paper titled “Concrete Problems in AI Safety” by Dario Amodei and others. This has been a very influential paper.

Machine Learning (ML)

Zipf's Law in NLP

According to Zipf's law, the frequency of a given word is dependent on the inverse of it's rank . Zipf's law is one of the many important laws that plays a significant part in natural language processing.

Machine Learning (ML)

Alternatives to CNN (Convolutional Neural Network)

We have explored Alternatives to CNN (Convolutional Neural Network) which includes Graph Neural Network and Capsule Neural Network.

Machine Learning (ML)

Adversarial Sample Transferability in Machine Learning: Attacks

We have discussed about what adversarial machine learning is and what transferability attacks are. The ideas are from Ian Goodfellow.

Machine Learning (ML)

Can Machine Learning (ML) be secure?

We answer the question "Can Machine Learning (ML) or Artificial Intelligence (AI) be secure?". We explore different types of attacks on Machine learning, defense strategies and more

Machine Learning (ML)

Application of BERT : Sentence semantic similarity

In this article, we have introduced another application of BERT for finding out whether a particular pair of sentences have the similar meaning or not.

Machine Learning (ML)

Application of BERT : Binary Text Classification

This article focused on implementation of one of the most widely used NLP Task "Binary Text classification " using BERT Language model and Pytorch framework.

Machine Learning (ML)

MobileNet V1 Architecture

MobileNet is an efficient and portable CNN architecture that is used in real world applications. We have explored the MobileNet V1 architecture in depth.

Machine Learning (ML)

Summary: The Case for Learned Index Structures

This outlines and evaluates the potential of a new approach to build indexes that is the merge of Algorithms/ Data Structures and Machine Learning.

Machine Learning (ML)

Adversarial Examples are not Bugs but are Features of AI

Adversarial Vulnerability is a direct result of our models' sensitivity to well-generalizing features in the data. We explored the research paper.

Machine Learning (ML)

The Indirect Convolution Algorithm

Indirect Convolution is as efficient as the GEMM primitive without the overhead of im2col transformations - instead of reshuffling the data, an indirection buffer is introduced.

Machine Learning (ML)

Summary: Learning to See by Moving

Research question: Is it also possible to learn useful features for a diverse set of visual tasks using any other form of supervision?