Month: September 2019

Updated Machine Learning Templates v12 for R

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a set of project templates that can be used to support modeling ML problems using R.

Version 12 of the templates contain minor adjustments and corrections to the prevision version of the templates. Also, the new templates added or updated the sample code to support:

You will find the R templates on the Machine Learning Project Templates page.

Web Scraping of O’Reilly Velocity Conference 2019 San Jose Using R

SUMMARY: The purpose of this project is to practice web scraping by extracting specific pieces of information from a website. The web scraping R code leverages the rvest package.

INTRODUCTION: The Velocity Conference covers the full range of skills, approaches, and technologies for building and managing large-scale, cloud-native systems. This web scraping script will automatically traverse through the entire web page and collect all links to the PDF and PPTX documents. The script will also download the documents as part of the scraping process.

Starting URLs:

The source code and HTML output can be found here on GitHub.
















Python Deep Learning Template v1 for Binary Classification

As I work on practicing and solving machine learning (ML) problems, I find myself repeating a set of steps and activities repeatedly.

Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a project template that can be used to support modeling binary classification problems using Python and the Kera framework.

Version 1 of the deep learning template is the first iteration and contains sample code segments for:

  • Preparing the deep learning modeling environment with a TensorFlow backend
  • Loading the data
  • Defining the Keras model
  • Compiling the model
  • Fitting the model on the dataset
  • Evaluating the model
  • Finalizing the model and making predictions

You will find the Python deep learning template on the Machine Learning Project Templates page.

Drucker on Managing Oneself, Part 3

In his book, Management Challenges for the 21st Century, Peter Drucker analyzed and discussed the new paradigms of management.

Although much of the discussion revolves around the perspective of the organization, these are my takeaways on how we can apply his teaching on our journey of being a knowledge worker.

For knowledge workers, understanding the factors that influence our performance is just as important as understanding our strengths. Like our strengths, how we perform is also individualized. Another word, our personality plays a major part in determining how we perform.

Drucker suggested we explore three questions in the quest of understanding how we perform.

  1. How do I perceive information?
  2. How do I learn?
  3. What are my values?

Am I a reader or a listener? We perceive information in different ways, and understanding our preference is crucial in being effective at what we do. The distinction between the reader and the listener is even more critical when it comes to our decision-making process. We should understand the difference and put ourselves in the best position possible to receive and process the information we need to make decisions.

Knowing the reader vs. listener preference is very much like knowing and working with our left-hand vs. right-hand preference. If we can work with our preferences, we get a better chance to amplify our effectiveness. When we work against our preferences, we stand to lose or even destroying our effectiveness.

The second thing to know how we perform is to know how we learn. There are probably several ways to learn, and, again, we will have our preferences. Some people learn by taking copious notes. Some people learn by hearing themselves talk. Some learn by doing, and some learn by reading and conceptualizing in their heads.

The above paths describe some of the ways of acquiring knowledge. There are other paths we take to learn from experience as well. Some learn better as loner, and some do better in a team setting. Some people do well under stress, and there are those who need a structured and predictable environment.

Moreover, some people perform and learn better as a decision-maker. Also, some would prefer to act as an adviser. The important thing suggested by Drucker is not to change ourselves too drastically, because that is unlikely to be successful. We should work hard to improve the way we perform and avoid putting ourselves in a situation or an environment where we will perform poorly.

Finally, Drucker reminded us that our values play the ultimate test in determining how we perform. Drucker called the values the “mirror test.” When we work in an organization with a values system that is incompatible with ours, we run a great risk of experiencing frustration and nonperformance.

Our strengths and performance are usually closely correlated. However, there is sometimes a conflict between a person’s values and the same person’s strengths. When there is a conflict between our strengths and values, we must take a close look and see where and why the discrepancies. If we do not resolve the discrepancies, we run the likely risk of low performance and low contribution.

Each one of us has something unique to offer. We all should put ourselves in the best position to perform by knowing our strengths and match them with our preferences to get the best results possible.