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.

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 [Explained]

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.

Software Engineering

Moore's second law or Rock's law

Moore's second law or Rock's law, states that the cost of a semiconductor chip fabrication plant doubles every four years. Research and Development, manufacturing and testing have increased steadily with each new generation of chips Rising manufacturing costs are an important consideration

computational model

Stack Machine: A computational model

A Stack Machine is a computational model that uses a last-in, first-out stack to hold short-lived temporary values. Most of its instructions assume that operands will be from the stack, and results placed in the stack. It has its own advantages such as it does not need any addressing

Software Engineering

RISC vs CISC architecture

RISC and CISC differ based on the following factors: approach to improve computing performance, hardware and Software focus and hardware specifications. In general, both are equally useful. CISC is commonly used in automation devices whereas RISC is used in video and image processing applications.

Software Engineering

Moore's law

Moore's law is the observation that the number of transistors in a dense integrated circuit doubles about every two years. It states that computing would dramatically increase in power, and decrease in relative cost, at an exponential rate. It is named after Gordon Earle Moore, founder of Intel

Algorithms

Manacher's Algorithm

Manacher's Algorithm is an efficient algorithm to find the longest palindromic substring in a given string in linear time and linear space complexity. It uses key ideas from dynamic programming to solve the problem efficiently.

Algorithms

Boyer Moore String Search Algorithm

Boyer Moore string search algorithm is an efficient string searching algorithm which was developed by Robert S. Boyer and J Strother Moore in 1977. The time complexity is linear in terms of length of data to be searched and preprocessing complexity is linear as well

gpu

Central Processing Unit (CPU) vs Graphics Processing Unit (GPU) vs Tensor Processing Unit (TPU)

Central Processing Unit (CPU), Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU) are processors with a specialized purpose and architecture. We have compared these in respect to Memory Subsystem Architecture, Compute Primitive, Performance, Purpose, Usage and Manufacturers.

Software Engineering

CAP theorem (Brewer's theorem)

The CAP theorem (or Brewer Theorem) is the idea that a distributed computing system is not able to provide partition tolerance, consistency and availability at the same time. It was developed in 2000 by Eric Allen Brewer. CAP theorem has applications in database and server infrastructure

instruction set

Explicitly parallel instruction computing (EPIC)

EPIC (Explicitly Parallel Instruction Computing) is a 64-bit microprocessor instruction set which is an improvement to the VLIW (Very Large Instruction Word) architecture. It has been developed by Intel and Hewlett Packard. It uses speculative loading, predication, and explicit parallelism

instruction set

Very long instruction word (VLIW)

Very long instruction word (VLIW) is an instruction set architecture designed to take full advantage of instruction level parallelism in form of pipelining, multiple processors, superscalar implementation and multiple independent operations. It has its advantages and disadvantages and is used widely

TensorFlow

How TensorFlow uses Graph data structure concepts?

In this article, we explain various concepts in TensorFlow such as tensors, dataflow graphs and several optimizations such as decision tree pruning and demonstrate the use of graph data structure and algorithm concepts in TensorFlow