Data Structures B-Tree Deletion A B-tree is a tree data structure that keeps data sorted and allows searches, insertions, and deletions in logarithmic amortized time. Deletion in a B Tree is similar to insertion. At first the node from which a value is to be deleted is searched. If found out, then the value is deleted.
Data Structures B-Tree : Searching and Insertion A B-tree is a self-balanced search tree data structure that keeps data sorted and allows searches, insertions, and deletions in logarithmic amortized time. Unlike self-balancing binary search trees, it is optimized for systems that read and write large blocks of data like database and file systems
Machine Learning (ML) Floating point operations per second (FLOPS) of Machine Learning models In this article, we take a look at the FLOPs values of various machine learning models like VGG19, VGG16, GoogleNet, ResNet18, ResNet34, ResNet50, ResNet152 and others. The FLOPS range from 19.6 billion to 0.72 billion.
Machine Learning (ML) Types of Activation Functions used in Machine Learning We explored the various types of activation functions that are used in Machine Learning including Identity function, Binary Step, Sigmoid, Tanh, ReLU, Leaky ReLU and SoftMax function. Activation function help the network use the useful information and suppress the irrelevant data points
Machine Learning (ML) Evolution of CNN Architectures: LeNet, AlexNet, ZFNet, GoogleNet, VGG and ResNet It all started with LeNet in 1998 and eventually, after nearly 15 years, lead to ground breaking models winning the ImageNet Large Scale Visual Recognition Challenge which includes AlexNet in 2012, ZFNet in 2013, GoogleNet in 2014, VGG in 2014, ResNet in 2015 to ensemble of previous models in 2016.
Data Structures Ternary Search Trees Ternary Search Tree is a special type of trie data structure and is widely used as an low memory alternative to trie in a vast range of applications like spell check and near neighbor searching. The average case time complexity is O(log N) for look-up, insertion and deletion operation.
Software Engineering Control Flow in Python In Control Flow, there are certain conditions which we put with the help of predefined syntaxes and the program then run in that order and chooses the condition as a output. Control statements include conditional and iteration statements like if else, while, do while and if elif and for loop
Machine Learning (ML) You only look once (YOLO) algorithm You only look once (YOLO) is a state-of-the-art, real-time object detection system. It is a fully convolutional network. On a Pascal Titan X, it processes images at 30 FPS and has a mAP of 57.9% on COCO. It has 75 convolutional layers with skip connections and upsampling layers and no pooling.
Machine Learning (ML) Single Shot Detection (SSD) Algorithm Single Shot MultiBox Detector (SSD) is an object detection algorithm that is a modification of the VGG16 architecture. It reached new records in terms of performance and precision for object detection tasks, scoring over 74% mAP at 59 frames per second on standard datasets such as PascalVOC and COCO
Machine Learning (ML) Overview of Object Detection in Computer Vision Object detection models are used to identify multiple relevant objects in a single image.The second significant advantage of object detection models versus image classification ones is that location of the objects is provided. Popular Object Detection Models are YOLO and SSD.
Machine Learning (ML) VGG16 architecture We have explored the VGG16 architecture in depth. VGGNet-16 consists of 16 convolutional layers and is very appealing because of its very uniform Architecture. Similar to AlexNet, it has only 3x3 convolutions, but lots of filters. It can be trained on 4 GPUs for 3 weeks.
Machine Learning (ML) Terms used in Neural Networks The common terms used in Neural Networks are Convolution, Max Pooling, Fully Connected Layer, Softmax Activation Function and Rectified Linear Units.
Machine Learning (ML) Feed Forward Neural Networks A feedforward neural network is an Artificial Neural Network in which connections between the nodes do not form a cycle. Learn about how it uses ReLU and other activation functions, perceptrons, early stopping, overfitting, and others. See the architecture of various Feed Forward Neural Networks
Machine Learning (ML) BLAS vs BLIS BLAS (Basic Linear Algebra Subprograms) and BLIS (BLAS Like Interface Software) are libraries that have revolutionized scientific computing by accelerating execution of mathematical operations on a vast range of platforms. In short, BLIS is the new generation alternative to BLAS
Python A Pythonic Way of PDF to Image Conversion Can a PDF be converted into a sequence of images through a program? If so which is the best technique to perform this conversion? This article answers the above questions.
Data Structures How many labeled and unlabeled binary tree can be there with N nodes? In this article, we see how many labeled and unlabeled binary trees we can have with N nodes. This is related to the Catalan Numbers. Binary Tree : A tree whose elements have 0 or 1 or 2 children is called a binary tree.
Algorithms Minimum operations to make GCD of array a multiple of k We are given an array and k, we need to find the minimum operations needed to make GCD of the array equal or multiple of k. Here an operation means either increment or decrement an array element by 1.
Machine Learning (ML) Types of Neural Network optimizations The types of neural network optimizations are weight pruning, structured pruning, convolution, fully-connected, structured group, structure ranking with activations like Lp norm, block pruning, model thinning, compression schedule, regularization, group lasso, group variance, quantization and others
similarity measurement Minkowski distance [Explained] Minkowski distance is a distance/ similarity measurement between two points in the normed vector space (N dimensional real space) and is a generalization of the Euclidean distance and the Manhattan distance. See the applications of Minkowshi distance and its visualization using an unit circle.
Machine Learning (ML) Principle of Sammon Mapping Sammon mapping (also known as Sammon projection) is an algorithm that maps a high dimensional data to lower dimensional data by preserving the structure of inter point distances in the original data. Learn why Sammon Mapping is better than Principal Component Analysis (PCA)
similarity measurement Damerau Levenshtein distance Damerau Levenshtein distance is a variant of Levenshtein distance which is a type of Edit distance. Damerau stated that the four operations in Damerau Levenshtein distance correspond to more than 80% of all human misspellings. It adds an extra operation named transposition to its set of operations
similarity measurement Levenshtein distance evenshtein distance is a type of Edit distance which is a large class of distance metric of measuring the dissimilarity between two strings by computing a minimum number of operations (from a set of operations) used to convert one string to another string. It is a way of pairwise string alignment.
Machine Learning (ML) Build / Install Eigen Library from source In this article, we explore the way to build and install Eigen library from source using cmake. Eigen is an efficient open-source C++ library for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers and related algorithms.
similarity measurement Edit distance Edit distance is a large class of distance metric of measuring the dissimilarity between two strings by computing a minimum number of operations (from a set of operations) used to convert one string to another string. It can be seen as a way of pairwise string alignment.
similarity measurement Euclidean vs Manhattan vs Chebyshev Distance Euclidean distance, Manhattan distance and Chebyshev distance are all distance metrics which compute a number based on two data points. All the three metrics are useful in various use cases and differ in some important aspects such as computation and real life usage.
