How To Turn Your Code from Good to Great
(CoverImage by WallPaperCave)
Contents
1. Introduction
2. R + RStudio
3. Python + VSCode
4. Conclusion
5. More Resources
1. Introduction
Modern day programmers write a lot of code. That’s part of the job. But one of the main principles of writing good code is the DRY principle: Don’t Repeat Yourself (see The DRY Principle: R Functions or The DRY Principle: Python Functions or Wikipedia or any number of other online sources). In essence, this principle effectively states that if you are going to write the same code twice, then don’t; instead, write the code in a function, then call the function twice.
Your mental models can both assist and limit your perceptions of the world, especially when you meet a person for the first time
(Image by ChristianChan on Shutterstock)
Our mental models are deeply engrained images of how we see the world and how we react in different scenarios and situations (McShane, Travaglione & Olekalns 2010, p. 91; Senge 2006, p. 164).
When I meet someone for the first time, my mental models greatly influence my perceptions of them: their words, their expressions, their reactions. And by extension, these mental models then influence my thoughts and actions in that situation: how I behave, how I speak, even my subconscious mannerisms. As a result, that first meeting can make for a pleasant first impression or an embarrassingly regrettable occasion.
Thoughts and Theory
A Review of the Historic, Modern, and Future Applications of this Special Form of Machine Learning
Contents
1. Introduction
2. Historic Developments (pre 1992)
— 2.1. Concurrent Developments
— — 2.1.1. Learning by Trial and Error
— — 2.1.2. The Problem of Optimal Control
— — 2.1.3. Temporal Difference Learning Methods
— 2.2. Combined Developments
3. Modern Developments (Post 1992)
— 3.1. Developments in Board Games
— 3.2. Developments in Computer Games
4. Current Developments
5. Future Developments
6. Conclusion
7. References
1. Introduction
Reinforcement Learning is not a new concept, but has been developed and matured over 70 years of academic rigour. Fundamentally, Reinforcement Learning is a method of machine learning by which an algorithm can make decisions…
A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition
Contents
This is Part 1 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.
Part 1 — Introduction and Overview ← Selected page
Part 2 — Literature Review
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries
Part 6 — Research Area 3: Surprising Trends
Part 7 — Research Area 4: Most Influential Indicator
Part 8 — Recommendations and Conclusions…
I like it 😊 Good work!
Perhaps a little philosophical at times. But still a nice Blog. Made me laugh quite a few times!
If you do choose to publish, I encourage you to remove the parts about 'deadline' and 'assessment' etc. Mr Joe Public doesn't need to know about those bits.
I like it 😊 Great work!
Try adding some headings 😉 It'll help guide the reader along.
Take a Look Under The Hood of Neural Network Architecture: Design and Build a Neural Network, from Scratch, in R, without using any Deep Learning Frameworks or Packages
Special thanks to: Alex Scriven
Contents:
1. Introduction
2. Background
3. Semantics
4. Set Up
5. Get Data
6. Check Data
7. Prepare the Data
8. Instantiate the Network
9. Initialise the Network
10. Forward Propagation
11. Calculate the Cost
12. Backward Propagation
13. Update Model Parameters
14. Run the Model End-to-End
15. Create Prediction
16. Conclusion
17. Post Script
1. Introduction
Modern-day Data Science techniques frequently use robust frameworks for designing and building machine learning solutions. In the R community, packages such as the tidyverse and the caret packages are frequently referenced; and within Python, packages such as numpy, pandas, sci-kit learn…
A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition
Contents
This is Part 8 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.
Part 1 — Introduction and Overview
Part 2 — Literature Review
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries
Part 6 — Research Area 3: Surprising Trends
Part 7 — Research Area 4: Most Influential Indicator
Part 8 — Recommendations and Conclusions ← Selected page…
A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition
Contents
This is Part 7 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.
Part 1 — Introduction and Overview
Part 2 — Literature Review
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries
Part 6 — Research Area 3: Surprising Trends
Part 7 — Research Area 4: Most Influential Indicator ← Selected page
Part 8 — Recommendations and Conclusions…
A Visual Data Exploration Research Project to Better Understand the Nuances of Our Global Nutrition
Contents
This is Part 6 of an 8-Part research project aiming to better understand the nuances of our global nutrition. It explores this topic through the utilisation of data visualisation and data science techniques. It is complimented by a Web App: ExploringUndernourishment, which is freely available to the public.
Part 1 — Introduction and Overview
Part 2 — Literature Review
Part 3 — Data Exploration
Part 4 — Research Area 1: General Trend
Part 5 — Research Area 2: Most Successful Countries
Part 6 — Research Area 3: Surprising Trends ← Selected page
Part 7 — Research Area 4: Most Influential Indicator
Part 8 — Recommendations and Conclusions…
Chris Mahoney
I’m a keen Data Scientist and Business Leader, interested in Innovation, Digitisation, Best Practice & Personal Development. Check me out: chrimaho.com
