Sunday 28 October 2018

Recreational Music Making (RMM) for Working Adults


Recreational Music Making (RMM) has been scientifically proven to help the U.S. workplace by:
Reducing employee stress
Reducing employee depression
Reducing employee burnout
Improving employee retention
  • Employee stress is expensive for companies and widespread. Research shows that the economic impact is estimated at $300 billion each year (Source: New York Times). Experts claim that 60 to 90 percent of doctor visits involve stress-related complaints.
  • Engaging in RMM reduces stress. RMM has been shown to reverse the body’s response to stress at the DNA level (Source: Dr. Barry Bittman).
  • Depression is widespread in the workforce and is expensive for companies. The economic impact of depression in the workplace is estimated at $34 billion annually—$11 billion for treatment, $11 billion in decreased productivity, and $12 billion in absenteeism. Depression affects about 19 million people, 70 percent of whom are in the workforce. (Figures are according to Braun Consulting News).
  • Engaging in RMM reduces depression. Recent research with long-term care workers showed reduced depression (21.8 percent) six weeks after the completion of an RMM program consisting of one hour per week. (Source: A 2003 study conducted by Trip Umbach Healthcare Consulting, Inc.)
  • RMM can help companies reduce turnover, saving them millions. The research with long-term care workers showed an 18.3 percent overall reduction of employee turnover by implementing an RMM program. The total annual savings was projected at $1.46 billion.
Every worker can participate in RMM. There are no physical limitations or requirements.

OpenCV - cannot find module cv2


Solution :

Step 1 :
$ conda update anaconda-navigator
$ conda update navigator-updater

Step 2:
$ pip install opencv-python

or

conda install opencv

Ref: StackOVerflow

Note : The above solution is tried on ubuntu 14.


Thursday 25 October 2018

Machine learning

Start the course with  following list of Lectures.

1. Machine Learning | Andrew Ng

2. cs321n