The presentation of theoretical material of the course is accompanied by a consideration of the possible schemes for parallel computing, in the practical part of the course the students supposed to perform a software implementation of evolutionary models using MPI technology and conducting numerical experiments to investigate the effectiveness of se...
Parallelizing Operations Using the lambda Function Setting up a MapReduce Simulation 10 Compressing and Concealing Data Using Compression Using Encryption 11 Working with Greedy Algorithms Using the change Function 12 Relying on Dynamic Programming Printing a Fibonacci Sequence Using Recursio...
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs. CareerServices ...
This lecture covers the concepts of parallelism, consistency models, and basic parallel programming techniques. Parallel Programming 2 This lecture covers the solutions for the consistency problem in parallel programming. Small Multiprocessors This lecture covers the implementation of small multiprocessors. ...
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs. CareerServices ...
Also, you will be able to implement Deep Learning Algorithms. Advanced Deep Learning and Computer Vision This Module discusses Advanced Deep Learning Concepts like Generating Images, Parallel Computing, Reinforcement Learning, Deploying Deep Learning Models. Distributed and Parallel Computing Deploying Deep ...
Moreover, the theory has to lead to computationally intensive algorithms, to guide the computer scientists into the field of soft- and hardware-architectures, especially with the focus on parallel computing. As shown in Sect. 1, the finite element method meets these conditions in an almost ...
Also, you will be able to implement Deep Learning Algorithms. Advanced Deep Learning and Computer Vision This Module discusses Advanced Deep Learning Concepts like Generating Images, Parallel Computing, Reinforcement Learning, Deploying Deep Learning Models. Distributed and Parallel Computing Deploying Deep ...
Online Capstone Project 39 Reviews 11 Hours per Week 2.0 / 5.0 DifficultyView Details Upper Division Elective CS 475 Introduction to Parallel Programming 38 Reviews 8 Hours per Week 1.8 / 5.0 DifficultyView Details Upper Division Elective CS 492 Mobile Software Development 11 Reviews 13 Hours per ...
This project has been included to help the learners to combine Spark SQL with ETL applications, perform real-time data analysis, deploy machine learning algorithms, perform batch analysis, build visualizations, and process graphs. CareerServices ...