Machine Learning (ML) Different Basic Operations in CNN We have explored the different operations in CNN (Convolution Neural Network) such as Convolution operation, Pooling, Flattening, Padding, Fully connected layers, Activation function (like Softmax) and Batch Normalization.
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) Adversarial Machine Learning Adversarial Machine learning is the technique which involves applying different methods in order to construct or generate examples that are meant to fool the machine learning model.
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) 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) 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) Optical Character Recognition (OCR) Optical Character Recognition or OCR is the technology that is used to convert characters or text that is either handwritten or printed in the form of paper into machine encoded text so that it can be saved digitally.
Machine Learning (ML) Batch Normalization Batch normalization is a technique used to increase the stability of a neural network. It helps our neural network to work with better speed and provide more efficient results.
Machine Learning (ML) Residual Neural Network (ResNet) Residual neural networks or commonly known as ResNets are the type of neural network that applies identity mapping to solve the vanishing gradient problem and perform better than RNN and CNN.
Machine Learning (ML) Types of Neural Networks Today, there are over 10 types of Neural Networks and each have a different central idea which makes them unique. We have explored all types in this article
Machine Learning (ML) Linear vs Logistic Regression We have explored the differences between Linear and Logistic regression in depth. We looking into the applications of Linear and Logistic regression along with a basic background.
