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How can you debug algorithms to ensure they are working?

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Debugging algorithms is a crucial skill for any artificial intelligence (AI) practitioner, whether you are developing machine learning models, natural language processing systems, computer vision applications, or any other AI-related solution. Algorithms are the set of rules or instructions that define how a problem is solved or a task is performed by a computer. However, algorithms can sometimes produce unexpected or incorrect results, or fail to run at all, due to various errors or bugs. In this article, you will learn some tips and techniques to help you debug algorithms effectively and ensure they are working as intended.

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Jake Brandt

?? Serverless/cloud principal engineer; ML algotrader; loves dogs.

1 Understand the problem

The first step to debug an algorithm is to understand the problem that it is trying to solve and the expected output or behavior. You should have a clear and precise specification of the input, output, and logic of the algorithm, and how it relates to the overall goal of the AI solution. You should also have some test cases or examples that can verify the correctness and functionality of the algorithm. By understanding the problem, you can narrow down the scope of the debugging and identify the potential sources of errors.

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Justin McGee Odger

Hybrid DNA | WayFinder Capital | Deedable - Doing amazing things with data ??
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Understanding the problem is a great way for AI engineers to collaborate with domain experts. I often note that a technical expert can rarely understand a problem to the same level that a business operator can. Both roles are integral to the development of an AI solution. Lean on your colleagues and your customers to understand the problem at hand, and involve them in the development process to create an optimal solution.

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Hristo Georgiev

AI, NLP specialist, owner of Langsoft - Sprachlernmittel
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Algorithms, are "yes" "no" instructions written on paper. When applied to Natural Language (NL) text, to analyse the text, algorithms are hand-checked on texts. One cannot debug algorithms. The term debugging is used to find errors of incorrect use of the Programming Language (PL). Debugging cannot find incorrect instructions to analyse NL text. For example, incorrect attribution of a word to Part of Speech. A programmer uses print to find out which instruction makes incorrect analysis by printing the hidden process through which the text is analysed. Print strays closed. It is opened only when needed to find an error.

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2 Use breakpoints and debuggers

Breakpoints and debuggers are tools that allow you to pause the execution of an algorithm at a certain point and inspect the values of the variables, parameters, and expressions. They can help you to trace the flow of the algorithm and find out where and why it deviates from the expected logic or output. You can use breakpoints and debuggers to test different scenarios, conditions, and inputs, and see how they affect the algorithm. You can also use them to modify the values of the variables or expressions and see how the algorithm reacts. Breakpoints and debuggers are available in most programming languages and IDEs, such as Python, Java, C++, Visual Studio, PyCharm, etc.

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3 Use print statements and logs

Print statements and logs are another way to monitor the execution of an algorithm and check the values of the variables, parameters, and expressions. They can help you to debug algorithms that are too complex or too fast to use breakpoints and debuggers, or that run on remote or distributed systems. You can use print statements and logs to display the values of the variables, parameters, and expressions at different stages of the algorithm, and compare them with the expected values. You can also use them to indicate the start and end of the algorithm, or any errors or exceptions that occur. Print statements and logs can be written to the console, a file, or a database, depending on your preference.

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Jake Brandt

?? Serverless/cloud principal engineer; ML algotrader; loves dogs.
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Perhaps it's a hot take; but while I do agree that "print statements" are technically a means to debug, to me it's a sign that testability was not considered during the design phase of the project, or if you follow an iterative Agile-inspired process such as Scrum, testability was not factored in as an acceptance criteria when the team committed to the current deliverable/story/item. However, let's fast-forward and consider a situation where you've determined you need to rely on output at runtime regardless - from personal experiences I highly recommend including the call stack if possible. It's super frustrating when you revisit logs later and have no idea where the code was that triggered the output!

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4 Use unit tests and code reviews

Unit tests and code reviews are methods to check the quality and functionality of an algorithm before deploying it to the production environment. Unit tests are automated tests that verify that each part or unit of the algorithm works as expected, given a specific input and output. Code reviews are manual inspections of the algorithm code by other developers or experts, who can provide feedback and suggestions for improvement. You can use unit tests and code reviews to detect and fix any bugs, errors, or inefficiencies in the algorithm, and to ensure that it meets the standards and requirements of the AI solution.

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Jake Brandt

?? Serverless/cloud principal engineer; ML algotrader; loves dogs.
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Far too often, you'll find that your issues (such as poor accuracy) are a result of upstream errors in your data preprocessing. By implementing unit testing (often paired with dependency injection), you apply a "contract" to granular levels of your system's behavior. You'd be surprised how often trivial issues in areas such as value normalization can be hidden as soon as a larger system is implemented around it. As a rule of thumb, if a comment exists to explain the reasoning behind a line or lines of code, a group of unit tests is worth the time in that area.

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5 Use visualization and simulation

Visualization and simulation are techniques to represent the algorithm or its output in a graphical or interactive way, which can help you to understand and debug it better. Visualization can help you to see the patterns, trends, or anomalies in the data, the results, or the performance of the algorithm, and to compare them with the expected or desired outcomes. Simulation can help you to test the algorithm in a virtual or realistic environment, and to observe how it behaves or responds to different situations, inputs, or parameters. You can use visualization and simulation tools, such as charts, graphs, diagrams, animations, dashboards, etc., to debug algorithms that involve complex or dynamic data or processes.

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6 Use online resources and communities

Online resources and communities are sources of information and support that can help you to debug algorithms and learn from others' experiences and solutions. Online resources include documentation, tutorials, guides, articles, blogs, videos, podcasts, etc., that can explain the concepts, principles, or best practices of algorithms and AI. Online communities include forums, groups, chats, Q&A sites, etc., that can provide answers, tips, feedback, or advice on specific problems or challenges related to algorithms and AI. You can use online resources and communities to debug algorithms that are new, unfamiliar, or difficult to you, or that require specialized knowledge or skills.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you debug algorithms to ensure they are working?

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3

4

5

6

7

1 Understand the problem

2 Use breakpoints and debuggers

3 Use print statements and logs

4 Use unit tests and code reviews

5 Use visualization and simulation

6 Use online resources and communities

7 Here’s what else to consider

Artificial Intelligence

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How can you debug algorithms to ensure they are working?

1

2

3

4

5

6

7

1 Understand the problem

2 Use breakpoints and debuggers

3 Use print statements and logs

4 Use unit tests and code reviews

5 Use visualization and simulation

6 Use online resources and communities

7 Here’s what else to consider

Artificial Intelligence

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