OpenGenus Foundation

machine learning

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 structure

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

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

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

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

VGG16 architecture

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. It is the most preferred choice in the community for extracting image features

machine learning

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

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

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

data structure

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.

algorithm

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

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

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

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

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.

similarity measurement

Chebyshev distance

Chebyshev distance is a distance metric which is the maximum absolute distance in one dimension of two N dimensional points. It has real world applications in Chess, Warehouse logistics and many other fields. It is known as Tchebychev distance, maximum metric, chessboard distance and L∞ metric.

similarity measurement

Euclidean distance (L2 norm)

Euclidean distance is the shortest distance between two points in an N dimensional space also known as Euclidean space. It is used as a common metric to measure the similarity between two data points and used in various fields such as geometry, data mining, deep learning and others.

similarity measurement

Manhattan distance (L1 norm)

Manhattan distance (L1 norm) is a distance metric between two points in a N dimensional vector space. It is the sum of the lengths of the projections of the line segment between the points onto the coordinate axes. It was introduced by Hermann Minkowski. It is used in regression analysis

tpu

What is Neural processing unit (NPU)?

A neural processing unit (NPU) is a microprocessor that specializes in the acceleration of machine learning algorithms. Examples include TPU by Google, NVDLA by Nvidia, EyeQ by Intel, Inferentia by Amazon, Ali-NPU by Alibaba, Kunlun by Baidu, Sophon by Bitmain, MLU by Cambricon, IPU by Graphcore

software engineering

Laws similar to Moore's Law

Laws similar to Moore's Law are Eroom's law, Engelbart's law, Haitz's law, Koomey's law, Carlson curve, Swanson's law, Pollack's rule, Wirth's law / Page's law / Gates's law / May's law / The great Moore's law compensator (TGMLC), Keck's law, Nielsen's Law and Dennard scaling.