Subscribe to DSC Newsletter

All Blog Posts Tagged 'networks' (27)

5G Networks Security Risks

In a modern world of advanced technologies, our life is literally impossible without cellular telecommunications. Today, smartphones and wireless broadband Internet are common things for everybody. And recently, all developments and innovations in cellular technology are not about the availability of mobile connectivity but about bringing it to a…

Continue

Added by David Balaban on October 24, 2019 at 9:32pm — No Comments

No Hope for 6-Degrees

researcher from the IICSE University DE, United States, has come up with trivial examples that put down the 6-Degree Theory, the theory that made Kevin Bacon appear in association with Academia online, Bacon.

 

Amongst those,…

Continue

Added by Marcia Ricci Pinheiro on January 5, 2019 at 8:00pm — No Comments

How the incorporation of prior information can accelerate the speed at which neural networks learn while simultaneously increasing accuracy

Deep neural nets typically operate on “raw data” of some kind, such as images, text, time series, etc., without the benefit of “derived” features. The idea is that because of their flexibility, neural networks can learn the features relevant to the problem at hand, be it a classification problem or an estimation problem.  Whether derived or learned, features are important. The challenge is in determining how one might use what one learned from the features in future work (staying…

Continue

Added by Jonathan Symonds on August 30, 2018 at 7:00am — No Comments

The Artificial Neural Networks handbook: Part 2

In last part we have seen the basics of Artificial intelligence and Artificial Neural Networks. As mentioned in the last part this part will be focused on applications of Artificial neural networks. ANN is very vast concept and we can find its…

Continue

Added by Jayesh Bapu Ahire on August 25, 2018 at 9:00pm — No Comments

Going Deeper: More Insight Into How and What Convolutional Neural Networks Learn

In my earlier post I discussed how performing topological data analysis on the weights learned by convolutional neural nets (CNN’s) can give insight into what is being learned and how it is being learned.  

The significance of this work can be summarized as follows:

  1. It…
Continue

Added by Jonathan Symonds on August 9, 2018 at 11:30am — No Comments

Building Recurrent Neural Networks in Tensorflow

Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …

Continue

Added by Ahmet Taspinar on July 5, 2018 at 11:48am — No Comments

Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks

TLDR: Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way. The implications of the finding are profound and can accelerate the development of a wide range of applications from self-driving everything to GDPR.

Introduction

Neural networks have demonstrated a great…

Continue

Added by Jonathan Symonds on June 21, 2018 at 9:30am — No Comments

Image identification using a convolutional neural network

This blog  explores a typical image identification task using a convolutional ("Deep Learning") neural network. For this purpose we will use a simple JavaCNN packageby D.Persson, and make our example small and concise using the Python scripting language. This example can also be rewritten in Java, Groovy, JRuby or any scripting language supported by the Java virtual machine.



This example will use images in the grayscale format (PGM). The name "PGM" is an acronym derived from…

Continue

Added by jwork.ORG on May 31, 2018 at 1:30pm — No Comments

Neural network classification of data using Smile

Data classification is the central data-mining technique used for sorting data, understanding of data and for performing outcome predictions. In this small blog we will use a library Smilecthat includes many methods for supervising and non-supervising data classification…

Continue

Added by jwork.ORG on March 13, 2018 at 4:00pm — No Comments

Using Neural Networks for sales prospecting

"No one wants to be sold but everyone wants to buy."

Most of us hate being sold. The moment we know someone is selling something, we keep our guards up. 

In the book, The Challenger Sale, authors Mathew Dixon and Brent Adamson surveyed over 6000 salespeople from around the world and found that ‘challenger salespeople’ outperformed every other group. Who are these challenger salespeople? These…

Continue

Added by Rudradeb Mitra on March 1, 2018 at 9:00pm — No Comments

Using Convolutional Neural Networks to detect features in sattelite images

In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.

It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods.  If you have been following the latest technical developments you probably know that CNN’s are…

Continue

Added by Ahmet Taspinar on December 4, 2017 at 5:00am — No Comments

Machine Learning Algorithms: Which One to Choose for Your Problem

When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don’t ease the choice.

In this article, I will try to explain basic concepts and give some intuition of using different…

Continue

Added by Luba Belokon on October 26, 2017 at 6:00am — No Comments

Time series classification with Tensorflow

Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some  domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. classification of EEG signals), then possible features would involve…

Continue

Added by Burak Himmetoglu on August 22, 2017 at 7:00am — 6 Comments

Generative Adversarial Networks (GANs): Engine and Applications


Generative adversarial networks (GANs) are a class of neural networks that are used in unsupervised machine learning. They help to solve such tasks as image generation from descriptions, getting high resolution images from low resolution ones, predicting which drug…

Continue

Added by Luba Belokon on August 17, 2017 at 6:30am — No Comments

Building Convolutional Neural Networks with Tensorflow

In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications.

The contents of this blog-post is as follows:

  1. Tensorflow basics:
    • 1.1 Constants and Variables
    • 1.2 Tensorflow Graphs and Sessions
    • 1.3 Placeholders and…
Continue

Added by Ahmet Taspinar on August 15, 2017 at 4:00am — No Comments

Recasting Java neural networks in Python

Many neural network applications implemented in Java, such as Neuroph, Encog and Joone, may look rather different when switching from the Java language to Python with the help of the DMelt computing environment. First of all, they look simpler. You can use your favorite Python tricks to load and display data. The Python coding is simpler for viewing and fast modifications. It does not require recompiling after each change. At the same time, the platform…

Continue

Added by jwork.ORG on July 29, 2017 at 1:00pm — No Comments

Deciphering the Neural Language Model

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail.

One of the assignments in…

Continue

Added by Burak Himmetoglu on December 17, 2016 at 10:00am — No Comments

Neural Networks: The Backpropagation algorithm in a picture

Here I present the backpropagation algorithm for a continuous target variable and no activation function in hidden layer: although simpler than the one used for the logistic cost function, it's a proficuous field for math lovers.

Added by Rubens Zimbres on November 19, 2016 at 9:30am — 3 Comments

Matrix Multiplication in Neural Networks

This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It's a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were used in the Matrices, same color as the Neural Network structure (bias, input, hidden, output) to make it easier to understand.…

Continue

Added by Rubens Zimbres on November 16, 2016 at 11:00am — 4 Comments

24 Neural Network Adjustements

Added by Rubens Zimbres on October 27, 2016 at 7:30am — No Comments

Monthly Archives

2019

2018

2017

2016

2015

2014

2013

2012

2011

1999

Videos

  • Add Videos
  • View All

© 2019   Data Science Central ®   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service