Machine Learning Strategies
Creating a machine learning system is not just about coding an algorithm and feeding it data; it's about understanding and troubleshooting its behavior and steering it towards better performance. This section has insights from deep learning applications in industry and strategies to efficiently improve models.
Understanding machine learning strategy can save significant amounts of time. By learning to diagnose and address specific issues within a system, you can avoid the common pitfall of aimless experimentation.
- Error Diagnosis: Learn to understand the errors a system is making. Is it a data issue, a model complexity issue, or something else?
- Directional Priority: Recognize which improvements will significantly impact your system's performance. Not all changes are equal.
- Advanced Contexts: Handle complex situations such as data mismatch or striving for human-level performance.
- Learning Paradigms: Know when to employ different methodologies like end-to-end learning, transfer learning, or multi-task learning.
Why ML Strategy is Crucial
In machine learning, it's nice if you can look at your system and say this piece of it is lacking. If you can figure out exactly what's wrong, and then have exactly one knob, or a specific set of knobs, that you can reach for to just solve that problem that is limiting the performance of the machine learning system, you can make a mediocre model into an excellent one.
There are a lot of ideas for how to improve the accuracy of your deep learning system:
- More Data: Sometimes more data is the key, but other times it's just a costly distraction.
- Diverse Training Set: Diversity can combat overfitting and improve generalization.
- Optimization Tweaks: Small changes in the optimization process, like switching to Adam or adjusting the learning rate, can yield big results.
- Network Adjustments: The size of the network or the addition/changes of regularization techniques like dropout can make or break your performance.
- Architectural Changes: Modifying the architecture, like activation functions or layer connections, can unlock new capabilities in your model.
This section will give you some strategies to help analyze your problem to go in a direction that will help you get better results.