Experiences of Developing Machine Learning Algorithms - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In the process of developing learning algorithms, anticipating potential issues is challenging. One recommended approach is to initiate application development by implementing a swift, straightforward, preliminary, quick, simple and dirty learning algorithm—such as logistic regression. Subsequently, conduct a bias-variance analysis to discern any shortcomings in the model and utilize these insights to inform the next steps. This may involve transitioning to a more intricate algorithm or considering the addition of more data, thereby guiding further refinement of the model, and then use that to decide what to do next. The process described above involves an iterative and incremental approach to model development, often referred to as the "iterative development cycle" or "iterative modeling":
This iterative approach allows you to incrementally improve your model, addressing issues as they arise. It also helps in avoiding unnecessary complexity at the outset, as you can gradually introduce more complexity only when needed. One way to gain good ML experiences is to work in a good AI/ML group so that you will involve in multiple (e.g. 10) projects. Involvement in multiple projects exposes individuals to a variety of real-world problems and applications of machine learning. Each project may involve different datasets, algorithms, and problem domains, providing a diverse set of experiences. ============================================
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