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) 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?
Machine Learning (ML) Wiener filter Wiener filter performs two main functions - it inverts the blur of the image and removes extra noise. It is particularly helpful when processing images that have been through a degradation filter.
Machine Learning (ML) Summary: High Performance Convolutional Neural Networks for Document Processing We have explored the paper "High Performance Convolutional Neural Networks for Document Processing" by Microsoft Research. It explores techniques to compute convolution layer in CNN faster.
Machine Learning (ML) Understanding Deep Image Representations by Inverting Them We have explore the problem that given an encoding of an image, to which extent is it possible to reconstruct the image itself? We have explored the paper "Understanding Deep Image Representations by Inverting Them".
Machine Learning (ML) Overview of Semantic Segmentation Semantic Segmentation is the process of labeling pixels present in an image into a specific class label. It is considered to be a classification process which classifies each pixel. The process of predicting each pixel in the class is known as dense prediction.
Software Engineering String Pool in Java In this article, I have explained the String Pool in Java and how to implement it in Java programming. I have covered basic concepts of this topic. Also, I have explain the diagram.
Machine Learning (ML) Multinomial/ Multimodal Naive Bayes Multimodal naive bayes is a specialized version of naive bayes designed to handle text documents using word counts as it's underlying method of calculating probability.
Machine Learning (ML) Byte Pair Encoding for Natural Language Processing (NLP) Byte Pair Encoding is originally a compression algorithm that was adapted for NLP usage. Byte Pair Encoding comes in handy for handling the vocabulary issue through a bottom-up process.
Software Engineering script and scriptreplay in linux script and scriptreplay are pre-installed commands in Linux. They are more like the utilities which allow the user to record their terminal session and to refer to it anytime he/she needs it.
Software Engineering Different Types Of Databases There are 6 different types of Databases which we have covered in depth in our article. It includes centralized, distributed, relational, NoSQL, Object Oriented and Hierarchical database.
Machine Learning (ML) John McCarthy, Man behind Garbage Collection John McCarthy was one of the most influential Computer Scientists in 1950s and a Professor at Stanford University for nearly 38 years. He is best known for developing LISP Programming Language, inventing Garbage Collection, coining the word Artificial Intelligence and much more.
