Machine Learning (ML) Restricted Boltzmann Machines Boltzmann Machines are bidirectionally connected networks of stochastic processing units. It can be used to learn important aspects of an unknown probability distribution based on samples from the distribution. We explore Gibbs Sampling and Contrastive Divergence.
Machine Learning (ML) Perceptron, the building block of modern AI A perceptron is an artificial neuron that essentially receives input from an input layer, processes the input with the help of an activation function (the Heaviside step function) and gives out the output in the form of either a 0 or 1. The perceptron was invented in 1957 by Frank Rosenblatt
C Programming Multithreading and pthread in C In this article, we have explored how the pthread library in C can be used to implement concepts of multithreading. A thread is a single sequence stream within in a process.
C Programming Semaphore in C Semaphore is a data handling technique which is very useful in process synchronization and multithreading. We used the POSIX semaphore library and use the functions sem_wait, sem_post, sem_open and sem_init for implementing semaphore in C.
multiplication Toom Cook method for multiplication Multiplication of two n-digits integers has time complexity at worst O(n^2).Toom-Cook algorithm is an algorithm for multiplying two n digit numbers in Θ(c(k)n^e) time complexity. The idea is based on divide-and-conquer technique
matrix multiplication Cannon’s algorithm for distributed matrix multiplication Cannon's algorithm is a distributed algorithm for matrix multiplication for two-dimensional meshes. It is especially suitable for computers laid out in an N × N mesh. The main advantage of the algorithm is that its storage requirements remain constant and are independent of the number of processors.
matrix multiplication Freivalds’ algorithm for verifying Matrix Multiplication Freivalds' algorithm is a probabilistic randomized algorithm used to verify matrix multiplication. Given three n x n matrices, Freivalds' algorithm determines in O(kn^2) whether the matrices are equal for a chosen k value with a probability of failure less than 2^-k.
matrix multiplication Russian peasant multiplication algorithm Russian peasant multiplication is an interesting way to multiply numbers that uses a process of halving and doubling without using multiplication operator. The idea is to double the first number and halve the second number repeatedly till the second number doesn’t become 1
matrix multiplication Strassen’s Matrix Multiplication algorithm Strassen’s Matrix Multiplication algorithm is the first algorithm to prove that matrix multiplication can be done at a time faster than O(N^3). It utilizes the strategy of divide and conquer to reduce the number of recursive multiplication calls from 8 to 7 and hence, the improvement.
C++ Most commonly used utilities in Standard Template Library in C++ This is an overview of the most commonly used utilities in Standard Template Library in C++. We have covered Iterator, Vector, Stack, Queue, Priority Queue, Map, Set and Pair utilities.
C++ Standard Template Library in C++ Standard Template Library in C++ is a pre-defined generic implementation of most widely used data structures and algorithms. By generic implementation it means that there is a single implementation of various Classes and Functions which works with multiple datatypes by the use of templates.
hacktoberfest Contribute to Hacktoberfest 2018 at OpenGenus Find super easy and effective contribution scopes at OpenGenus for Hacktoberfest 2018. It is organized by DigitalOcean, GitHub and Twilio. We are empowering developers to reach their potential at this special event.
clustering algorithm Introduction to Clustering Algorithms clustering is an unsupervised learning problem, since it seeks to classify or divide a dataset based on attributes of the points themselves rather than any given labels.
clustering algorithm K-means Clustering k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. The algorithm will categorize the items into k groups of similarity, Initialize k means with random values For a given number of iterations: Iterate through
clustering algorithm DBSCAN Clustering Algorithm Density-based spatial clustering of applications with noise is a data clustering unsupervised algorithm. The key idea is to divide the dataset into n ponts and cluster it depending on the similarity or closeness of some parameter.
linked list Flattening a Linked List In this article, we explored an algorithm to flatten a linked list where each node has two pointers with one to another linked list. Our approach explored a novel application of the merge component of merge sort to solve this problem.
linked list Find the middle element of a singly Linked List In this article, we will explore two approaches to find the middle element of a singly linked list. In one approach, we will use one traversal to count the number of elements and the other to find the middle element. The second approach is to use one traversal and concept of fast and slow pointers.
Algorithms Graph Coloring Greedy Algorithm [O(V^2 + E) time complexity] In this article, we have explored the greedy algorithm for graph colouring. graph coloring is a special case of graph labeling ; it is an assignment of labels traditionally called "colors" to elements of a graph subject to certain constraints.
Algorithms Wigderson Graph Colouring Algorithm in O(N+M) time Wigderson Algorithm is a graph colouring algorithm to color any n-vertex 3-colorable graph with O(√n) colors, and more generally to color any k-colorable graph. In this article, we have explored this wonderful graph colouring article in depth.
Artificial Intelligence L1 and L2 Regularization Methods A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Lasso shrinks the less important feature’s coefficient to zero
Artificial Intelligence Gradient descent: Mathematical view Gradient descent algorithm is one of the most popuarl algorithms for finding optimal parameters for most machine learning models including neural networks. The basic method that this algorithm uses is to find optimal values for the parameters that define your ‘cost function’.
Artificial Intelligence Regularization Regularization is a method used to reduce the variance of your model and increase the bias. It is used when your model overfits the training data. Another method to do regularization is called Lasso regression. This is the solution to Biase-Variance Dilemma.
Artificial Intelligence Cross Validation Cross Validation is a procedure used to evaluate your machine learning model on limited sample of data. With the help of this, we can actually tell how well our model performs on unseen data. Other variants are stratified cross validation and leave one out cross validation. Learn through an example
Artificial Intelligence Decision Trees Decision Tree is a popular machine learning algorithm mainly used for classification. Concepts of entropy and information gain are required to apply decision tree for a data set. It is used for non-linear classification and regression Learn through an example
red black tree Red Black Tree: Deletion We will explore the deletion operation on a Red Black tree in the session. Deleting a value in Red Black tree takes O(log N) time complexity and O(N) space complexity. A red–black tree is a kind of self-balancing binary search tree in computer science.
