Agnes  Sauer

Agnes Sauer


Lessons from War: What I learned as a Military Analyst

I was an Intelligence Specialist in the US Navy for 6 years and served in the reserves and active duty in Afghanistan and Africa. Military intelligence training is one of the single best ways to learn analytical reasoning, data analysis, and how to develop the ability to present your findings to large groups of individuals.

We were taught everything you need to be a top-grade analyst. Here are three key lessons that I have kept with me. These lessons can apply to all individuals in the data analysis field.

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I’m the one in the back with the kindle in his hand.

Three Lessons for Analysts

Lesson 1: People May Die

In the military world, if you make a mistake, someone may die. So, you better make sure your information is correct. In the civilian world, the stakes aren’t as high but the lesson holds true. It may not be a human life that is at stake but it may be an important sales deal or a successful marketing campaign. Double-check everything.

How to Apply:

  • **Come Back Fresh: **After completing your project, go take a walk or otherwise distract yourself so that you can come with fresh eyes. As we work on a project our brain starts to fatigue and we are more likely to miss small errors.
  • **Ask a Buddy: **Before sharing your project, take it to your buddy and ask them for their opinion. Ask them to evaluate it as critically as possible — it’s even better if you can give them a list of prompts — things like “Are my visuals clear?” or “Can you double-check my math?”
  • **Check your Sources: **Be critical with data sources. Explore their methodology and ensure that you are communicating the correct information from that data.

Lesson 2: No One Cares

Attention is finite. In the military world when I gave a presentation to admirals or captains, I could see their minds were somewhere else. They were thinking about the next mission. CEOs and other leaders in your organization are doing the same thing. They’re thinking about the next deal or the last email they received. **You have to be able to attract and hold the attention of your stakeholders. **You need to _make _people care.

#tech #data-analysis #military #data #analytics #data analysis

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Lessons from War: What I learned as a Military Analyst
Matteo  Renner

Matteo Renner


The Most Important Programming Lesson I Ever Learned

In the fall of 2012, I walked into my graduate advisor’s office and asked her which computer science class she recommended for me to enroll in. I explained that I was a complete novice in programming. She suggested Introduction to C Programming.

After attending a few lectures, I discover that the majority of the students I spoke to in this introductorycourse had some prior experience in programming.

Six weeks and 80 hours of work later, I dropped the course.

Enter spring semester of 2013. I enrolled in an easier computer science course, Introduction to Computer Programming via the Web. I breezed through the first quarter of the course, executing HTML and CSS with ease. Then, we started Javascript (JS). That feeling of constant anxiety and stress from my previous computer science course returned in full fashion. It was too late in the semester to drop the course, so I asked a friend for help.

#debugging #learning-to-code #learning-to-program #computer-science-basics #how-to-start-learning-to-code #python-programming #learn-javascript #learn-python #web-monetization

Jerad  Bailey

Jerad Bailey


Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Jackson  Crist

Jackson Crist


Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning

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