“DEEP LEARNING PROJECT” starts now. I believe it works in digital marketing and economic analysis


As the new year starts,  I would like to set up a new project of my company.  This is beneficial not only for my company, but also readers of the article because this project will provide good examples of predictive analytics and implementation of new tools as well as platforms. The new project is called “Deep Learning project” because “Deep Learning” is used as a core calculation engine in the project.  Through the project,  I would like to create “predictive analytics environment”. Let me explain the details.


1.What is the goal of the project?

There are three goals of the project.

  • Obtain knowledge and expertise of predictive analytics
  • Obtain solutions for data-driven management
  • Obtain basic knowledge of Deep Learning

As big data are available more and more, we need to know how to consume big data to get insight from them so that we can make better business decisions.  Predictive analytics is a key for data-driven management as it can make predictions “What comes next?” based on data. I hope you can obtain expertise of predictive analytics by reading my articles about the project. I believe it is good and important for us  as we are in the digital economy now and in future.


2.Why is “Deep Learning” used in the project?

Since the November last year, I tried “Deep Learning” many times to perform predictive analytics. I found that it is very accurate.  It is sometimes said that It requires too much time to solve problems. But in my case, I can solve many problems within 3 hours. I consider “Deep Learning” can solve the problems within a reasonable time. In the project I would like to develop the skills of tuning parameters in an effective manner as “Deep Learning” requires several parameters setting such as the number of hidden layers. I would like to focus on how number of layers, number of neurons,  activate functions, regularization, drop-out  can be set according to datasets. I think they are key to develop predictive models with good accuracy.  I have challenged MNIST hand-written digit classifications and our error rate has been improved to 1.9%. This is done by H2O, an awesome analytic tool, and MAC Air11 which is just a normal laptop PC.   I would like to set my cluster on AWS  in order to improve our error rate more. “Spark” is one of the candidates to set up a cluster. It is an open source.


3. What businesses can benefit from introducing “Deep Learning “?

“Deep Learning ” is very flexible. Therefore, it can be applied to many problems cross industries.  Healthcare, financial, retails, travels, food and beverage might be benefit from introducing “Deep Learning “.  Governments could benefit, too. In the project, I would like to focus these areas as follows.

  • Digital marketing
  • Economic analysis

I would like to create a database to store the data to be analyzed, first. Once it is created,  I perform predictive analytics on “Digital marketing” and “Economic analysis”.  Best practices will be shared with you to reach our goal “Obtain knowledge and expertise of predictive analytics” here.  Deep Learning is relatively new to apply both of the problems.  So I expect new insight will be obtained. For digital marketing,  I would like to focus on social media and measurement of effectiveness of digital marketing strategies.  “Natural language processing” has been developed recently at astonishing speed.  So I believe there could be a good way to analyze text data.  If you have any suggestions on predictive analytics in digital marketing,  could you let me know?  It is always welcome!


I use open source software to create an environment of predictive analytics. Therefore, it is very easy for you to create a similar environment on your system/cloud. I believe open source is a key to develop superior predictive models as everyone can participate in the project.  You do not need to pay any fee to introduce tools which are used in the project as they are open source. Ownership of the problems should be ours, rather than software vendors.  Why don’t you join us and enjoy it! If you want to receive update the project, could you sing up here?



Notice: TOSHI STATS SDN. BHD. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

Can you be next “Mark Zuckerberg” with open source software?


I like open source software because it is  almost free to use,  modify and distribute. For example,  I use “R language” for data analysis as I can share code to anyone without cost.  R is an example of open source software. When I used to be a risk manager more than 10 years ago, I used MATLAB.  This is an awesome software for data analysis. However, we need to buy a license to use it. So I cannot recommend it for everyone.  But I can do that for R as it is free.


Open source software is strong enough to change the landscape of developing computer programs. Especially I look at the movement driven by Facebook, it looks like a big tsunami to take over the industry. It has more than 200 open source software projects from mobile application development to artificial intelligence according to the article. Mark Zuckerberg,  Founder and CEO of Facebook, have been taking initiative open source movement for many years.  For new start-up, it is very good and helpful because


1.  It accelerates development of applications

Because startups usually do not have enough resources to develop the applications from scratch, it is very helpful for them to use open source software. All they should do is modify the software to make applications. Facebook is also built by using open source software, although it becomes one of the biggest IT companies in the world.


2. There are more choices provided by open source softwares

When there are several kinds of open sources for specific purposes, we can choose the best one for our own purpose. All we should do is  to assess each of them.  For example, when you are interested in artificial intelligence, there are many major open source softwares,  such as TheanoPylearn2Torch, OpenDeep, Chainer and so on.  Each of them is a little different in terms of functionality and structures. Therefore, we should choose the best one for our own purpose. When we have the best choice. it allows us to develop applications rapidly and effectively.


3.  Open source softwares can lower the entrance barriers

It is usually difficult for start-ups to develop complex programs, such as deep learning, from scratch. But supported by open source software, start-ups can learn and develop the applications at the same time. It is very important in the digital economy as the supply of experts in such fields are always less than the demands in labor markets.



Going forward, I would like to develop an economic analysis system by using open source software and make it available for everyone who is interested in.  I hope everyone can analyze the economy in his/her own country by him/herself in the business.