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.
I’m the one in the back with the kindle in his hand.
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:
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
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.
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
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.
#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany
Visit Blog- https://www.xplace.com/article/8743
#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert
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:
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
SISGAIN is one of the top e-Learning software companies in New York, USA. Develop Education Technology based, mobile application for e-learning from SISGAIN. We Develop User Friendly Education App and Provide e-learning web portals development Service. Get Free Quote, Instant Support & End to End Solution. SISGAIN has been developing educational software and provides e-learning application development services for US & UK clients. For more information call us at +18444455767 or email us at firstname.lastname@example.org
#learning development companies #development of software for e-learning #top e-learning software companies #e-learning web portals #mobile applications for e-learning #e-learning product development