OpenGenus Foundation - OpenGenus IQ

When to use Recurrent Neural Networks (RNN)?

Recurrent Neural Networks (RNNs) are designed to work with sequence prediction problems. RNNs can be used on Text data, Speech data, Classification prediction problems, Regression prediction problems and Generative models. Sequence prediction problems come in many forms.

machine learning

When to use Convolutional Neural Networks (CNN)?

Use CNN for data with a spatial relationship. Convolutional Neural Networks (CNNs) are designed to map image data to an output variable. They have proven so effective that they are the ready to use method for any type of prediction problem involving image data as an input

tensorflow

Run TensorFlow Convolutional Neural Network (TF CNN) benchmarks in CPU

We will walk you through running the official benchmark of (TF CNN benchmark) TensorFlow for Convolutional Neural Network on your machine. The process is simple and we have divided it into three simple steps: install tensorflow, get the benchmarking code and run the benchmark and observe results

data science

Basic Data Science concepts everyone needs to know

In this article, we explored some of the basic data science concepts everyone needs to understand to ace things in real life. We demonstrated basic statistic measurements such as median, bayesian statistics, probability distribution, dimensionality reduction and over and under sampling of data.

pattern searching

Rabin-Karp Pattern Searching Algorithm

Rabin-Karp Algorithm is an efficient string pattern searching algorithm that utilizes the technique of hashing to search for patterns in a string in linear time by using a clever way of calculating hashes. This algorithm has been developed by Richard M. Karp and Michael O. Rabin in 1987.

machine learning

Difference between MKL, MKL ML and MKL DNN

In this article, we give the relation between MKL, MKL ML and MKL DNN. MKL is a closed sourced BLAS library while MKL ML is an open-source BLAS library which is actually a subset of MKL. MKL DNN is an open source library used to optimize Deep Neural Network operations and depends on a BLAS library.

eigen

What is Eigen C++ Library?

Eigen is an efficient open-source C++ library for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers and related algorithms. It has been developed by two great developers namely Benoit Jacob and Gael Guennebaud. It has support for compilers like GCC

openblas

Install OpenBLAS from source

In this article, we will guide you to install OpenBLAS from source in three simple steps. OpenBLAS is an open source optimized BLAS (Basic Linear Algebra Subprograms) library based on GotoBLAS2 1.13 BSD version.

openmp

Install OpenMP from source

In this guide, we will walk you through the installation steps of OpenMP from source. Open Multi-Processing is a library that is used to make programs execute in a parallel fashion. It supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran on most platforms

genetic algorithm

Basics of Genetic Algorithms

A genetic algorithm is a search heuristic (related to making guesses) algorithm that is inspired by Charles Darwin’s theory of natural evolution. We have explained the basic concepts of genetic algorithms including initial population, fitness function, selection, crossover and mutation.

verilog

Basics of Verilog Programming

we demonstrate the key components of Verilog Programming for FPGA such as modules, ports, drivers, reg, wire, operators, conditional operators, begin, end, always block, posedge, blocking, non blocking statements, connecting modules and others. With the knowledge, you can stimulate basic circuits

machine learning

Building a Convolution Neural Network (CNN) for handwritten digit recognition in Python using Keras

We built a Convolution Neural Network (CNN) for handwritten digit recognition from scratch in python. We will be using Keras API with TensorFlow backend and use handwritten digits dataset from Kaggle.

fpga

Verilog vs VHDL (VHSIC Hardware Description Language)

Verilog and VHDL (VHSIC Hardware Description Language) are both hardware description language for hardware modeling and we have compared them. Verilog is used extensively for production level projects and in Industry. VHDL is great for beginners who are yet to develop Hardware Design Principles

fpga

Structure of Field Programmable Gate Array (FPGA)

A field programmable gate array (FPGA) consists of Input/ Output Pads, Configurable Logic Blocks (CLB), Connection Blocks, Switch Blocks and Channel Width. A field programmable gate array is an integrated circuit designed to be configured by anyone for various purposes like hardware stimulation

fpga

Field Programmable Gate Array (FPGA)

A field programmable gate array (FPGA) is an integrated circuit designed to be configured by anyone for various purposes like hardware stimulation. FPGA is defined by two keywords namely field programmable and gate array. We have provided applications, manufacturers and why to use FPGA

machine learning

Why Principal Component Analysis (PCA) works?

We have demonstrated how and why Principal Component Analysis (PCA) works using the intuition behind the common operations used in the algorithm such as Variance, Covariance, Eigenvectors and Eigenvalues. Eigenvectors represent directions while Eigenvalues represent magnitude the importance

machine learning

Algorithm of Principal Component Analysis (PCA)

The algorithm of Principal Component Analysis (PCA) is based on a few mathematical ideas namely Variance, Convariance, Eigen Vectors and Eigen values. The algorithm is of eight simple steps including preparing the data set, calculating the covariance matrix, eigen vectors and values, new feature set

machine learning

Basic Ideas of Principal component analysis

Principal component analysis (PCA) is a technique to bring out strong patterns in a dataset by supressing variations. It is used to clean data sets to make it easy to explore and analyse. We have demonstrated an example of 17 dimensions and given the basic intuition of PCA

machine learning

Top deep learning frameworks to explore

In this article, we have explored some of the top Deep Learning frameworks that are out there and you should definitely try out. Some of them are TensorFlow, Keras, Caffe, Caffe2, MXNet, CNTK, BigDL, Torch, PyTorch, deeplearn.js and others

machine learning

Popular Datasets in Machine Learning

Data sets are important in Machine learning as the more better data we have, the better the model. The various popular data sets available for machine learning are ImageNet, MNIST, NIST, CIFAR-10 and YouTube 8M.

tensorflow

Key ideas in TensorFlow

TensorFlow is a popular Deep Learning library that is used to build Deep Learning models and is backed by Google. A few key ideas of TensorFlow are tensor, distributed computing, kernel abstraction, operation abstraction, computational graph, automatic gradient computation and others

tensorflow

Build and install TensorFlow from source with MKL DNN support and AVX enabled

In this guide, we will walk you through building and installing TensorFlow from source with support for MKL DNN and with AVX enabled. By following the six simple steps, you can build and install TensorFlow from source in 20 minutes

machine learning

Differences between Torch and PyTorch deep learning libraries

We have explored some of the differences between two popular frameworks namely Torch and PyTorch from the view of common origin, current development status, source code and implementation, usage, performance and ONNX support. As development of Torch has been paused, you should go with PyTorch

openmp

OpenMP clauses: firstprivate, lastprivate, ordered

There are three basic OpenMP clauses namely firstprivate, lastprivate and ordered. firstprivate clause is used to initialize a variable from the serial part of the code and private clause doesn't initialize the variable

openmp

Basic OpenMP functions

There are 3 basic functions in OpenMP namely omp_get_thread_num, omp_set_num_threads (nthreads) and omp_get_num_threads. We have given a basic C/ C++ example to demonstrate the use of the function and the observation of the output as well