Search result for Network algorithms Online Courses & Certifications
FollowFilter ![]()
Filter ![]()
Showing 165 courses
By Category ![]()
By Subject![]()
By Language![]()
By Duration![]()
By Price![]()
Get Course Alerts by Email
Advanced Machine Learning & Data Analysis Projects Bootcamp
by Mammoth Interactive- 4.4
-
![]()
![]()
![]()
![]()
![]()
20.5 hours on-demand video
Computational graphs consist of a network of connected nodes (often called neurons). You will learn to figure out which kind of model scheme works best and what kinds of algorithms work best for the problem you’re trying to solve....
$12.99
Natural Language Processing with Deep Learning in Python
by Lazy Programmer Team- 4.7
-
![]()
![]()
![]()
![]()
![]()
12 hours on-demand video
For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers. My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch...
$39.99
Networks Illustrated: Principles without Calculus
by Christopher Brinton , Mung Chiang- 4.4
-
![]()
![]()
![]()
![]()
![]()
Approx. 24 hours to complete
While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. Network Is Expensive & Divide and Conquer Transport & Network Layers...
Advanced Data Structures in Java
by Leo Porter , Mia Minnes , Christine Alvarado- 4.8
-
![]()
![]()
![]()
![]()
![]()
Approx. 29 hours to complete
How does an internet router forward packets of network traffic to minimize delay? You will develop, implement, and analyze algorithms for working with this data to solve real world problems. When I Struggled: Remembering classical algorithms...
Machine Learning With TensorFlow: The Practical Guide
by Eduonix Learning Solutions- 3.9
-
![]()
![]()
![]()
![]()
![]()
6.5 hours on-demand video
This course also covers various algorithms like linear regression, logistic regression, NN regression, K-Means algorithm and others. Different algorithms like linear regression, logistic regression, clustering, K-means algorithm, kernels and many more...
$9.99
Transform 3D Game To AR Multiplayer :Unity C# Vuforia Photon
by Brinis Hamza- 3.6
-
![]()
![]()
![]()
![]()
![]()
3.5 hours on-demand video
Network Synchronization Algorithms...
$9.99
Graph Theory Algorithms
by William Fiset- 4.6
-
![]()
![]()
![]()
![]()
![]()
9 hours on-demand video
A complete overview of graph theory algorithms in computer science and mathematics. This course provides a complete introduction to Graph Theory algorithms in computer science. Various tree algorithms including: the height or a tree, finding the center of a tree, rooting a tree, and etc. Various network flow algorithms including: Edmonds-Karp, Capacity Scaling, and Dinic's algorithm...
$21.99
Related searches
Hybrid Python3|Swift4 Applications
by Brian Rouse- 4.1
-
![]()
![]()
![]()
![]()
![]()
1 hour on-demand video
You will learn iPython enough to implement algorithms used in Data Science with little effort. In your final project, you will build BIRADS, a Breast Imaging-Reporting and Data System that takes the output data from a Neural Network and assigns a BI-RADS Category given input from the following features....
$12.99
Big Data Analysis with Scala and Spark
by Prof. Heather Miller- 4.7
-
![]()
![]()
![]()
![]()
![]()
Approx. 28 hours to complete
Through hands-on examples in Spark and Scala, we'll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance. - express algorithms for data analysis in a functional style,...
Deep Learning Regression with R
by Diego Fernandez- 3.4
-
![]()
![]()
![]()
![]()
![]()
4 hours on-demand video
Minimize recurrent neural network vanishing gradient problem through long short-term memory units. For algorithm training, you’ll define optimal parameters estimation or fine tuning, bias-variance trade-off, optimal model complexity and artificial neural network regularization. For artificial neural network regularization, you’ll define node connection weights, visible and hidden layers dropout fractions, stochastic gradient descent algorithm learning and momentum rates....
$9.99
Previous
