A Data Science Central Community

I was working on the post related to the relation between IFS fractals and Analytical Probability density, when an IT manager contacted me asking an informal opinion about a tool for Neural Networks.

I was working on the post related to the relation between IFS fractals and Analytical Probability density, when an IT manager contacted me asking an informal opinion about a tool for Neural Networks.

He told me: “we are evaluating two different tools to perform general purpose data mining tasks, and we are oriented toward the tool xxx because the neural network module seems more flexible”.

My first reaction was: “sorry, but you have to solve a specific problem (the problem is a little bit complex to explain but it is a classical *inversion function* problem) and you are looking for a generic suite: I’m sincerely confused!”

He justified himself telling “If I buy a generic suite I can reuse it in a next time for other problems!”: **bloody managers! **Always focused to economize 1 $ today to loose 10$ tomorrow :)

As you can imagine the discussion fired me, so I started to investigate about this “flexibility capabilities” (this is for me the key point to evaluate a solution) and I began long emails exchange with this enterprising manager.

Let me sum up the main questions/answers about this module.

The suite has a nice GUI where you can combine the algorithms like lego-bricks. (it is very similar to WEKA’s gui).

Here you are the key points:

As usual, instead of thousands words I prefer get **direct and concrete proof about my opinion**, because my motto is : “don’t say I can do it in this way or in this way, but just do it!” (…hoping that it makes sense in English!).

So during this weekend I decided to implement in Mathematica a custom tool to play with Back Propagation Neural Networks having at least the same features described above.

Here you are the general-purpose Neural Net Application I built with mathematica: different colors for neurons to depict different energy levels., monitor to follow the learning process, fully customization for energy cost function, activation functions: |

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

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

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge