What do you do if you want to enhance your logical reasoning skills in a Machine Learning career?
Logical reasoning is the ability to analyze, evaluate, and infer from data and arguments. It is a crucial skill for any Machine Learning (ML) professional, as it helps them design, implement, and test algorithms and models that solve complex problems. However, logical reasoning is not something that you can master overnight. It requires practice, feedback, and exposure to different domains and scenarios. In this article, you will learn some tips and resources to enhance your logical reasoning skills in a ML career.
One of the most fun and effective ways to improve your logical reasoning skills is to solve puzzles and games that challenge your brain. These can range from classic logic puzzles like Sudoku, crosswords, and riddles, to more advanced ones like chess, Go, and Rubik's cube. Puzzles and games can help you develop your logical thinking, pattern recognition, and problem-solving abilities, as well as your creativity and concentration. They can also expose you to different types of logic, such as deductive, inductive, and abductive. You can find plenty of puzzles and games online, in books, or in apps, and try to solve them regularly.
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Melek Elloumi
I help Students ACE their PFE with high honors | PhD Student in Computer Vision | Software Engineer | Ex-Youtuber
Growing up playing puzzle games, I can attest that they help view problems from a different angle and develop better logical reasoning. I recommend the Professor Layton series for a variety of puzzles. As for machine learning, "while true: learn()" is a great game for beginners in this field. It simulates what machine learning models do and provide you classification problems that you have to solve under certain constraints (accuracy, number of blocks, ressource used, inference time).
Another way to enhance your logical reasoning skills is to learn from experts in ML and related fields. You can do this by reading books, articles, blogs, and papers that explain the concepts, methods, and applications of ML in a clear and logical way. You can also watch videos, podcasts, webinars, and courses that demonstrate how experts use ML to solve real-world problems. Learning from experts can help you expand your knowledge, understand different perspectives, and discover best practices and tips for ML. You can also interact with experts and peers through online forums, communities, and events, and ask questions, share insights, and get feedback.
A third way to enhance your logical reasoning skills is to apply logic to data. This means using data to support or refute hypotheses, arguments, and decisions. You can do this by collecting, cleaning, exploring, and visualizing data, and then applying various ML techniques to analyze and model data. You can also use logic to interpret and communicate the results and implications of your ML models. Applying logic to data can help you develop your analytical, critical, and inferential skills, as well as your intuition and curiosity. You can use various tools and platforms, such as Python, R, TensorFlow, and Kaggle, to work with data and ML.
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Melek Elloumi
I help Students ACE their PFE with high honors | PhD Student in Computer Vision | Software Engineer | Ex-Youtuber
Applying logic to data requires time to develop as you experience with more data in different ML problems. It starts by having a hypothese that you confirm or refute with experiments which then gives an idea on how the model interpreted the data it has. Once the hypothese is verified, it becomes a transferrable logic piece that you can apply in other similar problems. The logic will differ depending on the data type, the model architecture and the prediction task. One example of intuition in semantic segmentation, objects with different colour than the background are generally segmented better.
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Avneet Singh
Assistant Manager @ EXL | Data Analytics📊 | Business Analytics | Automation | MySQL
Brush up on your mathematical skills, especially in areas like linear algebra, calculus, probability, and statistics. These are essential for understanding the underlying principles of machine learning algorithms.
A fourth way to enhance your logical reasoning skills is to review your logic. This means checking the validity, soundness, and coherence of your logic, as well as identifying and avoiding logical fallacies and biases. You can do this by reviewing your own work and logic, as well as the work and logic of others. You can also use tools and frameworks, such as flowcharts, diagrams, pseudocode, and if-then-else statements, to structure and visualize your logic. Reviewing your logic can help you improve your accuracy, clarity, and consistency, as well as your confidence and credibility.
A fifth way to enhance your logical reasoning skills is to challenge your logic. This means exposing your logic to different scenarios, domains, and perspectives, and testing its robustness, flexibility, and applicability. You can do this by working on diverse and complex ML projects, either individually or in teams, and trying to solve them using different approaches and methods. You can also seek feedback and criticism from others, and be open to different opinions and viewpoints. Challenging your logic can help you expand your horizons, learn from your mistakes, and adapt to changing situations and requirements.
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