Basic Recipe for Machine Learning (C2W1L03)  Summary and Q&A
TL;DR
This video provides a basic recipe for diagnosing and improving bias and variance problems in machine learning algorithms.
Key Insights
 🎰 Diagnosing bias and variance problems is crucial in improving machine learning algorithms.
 ✋ High bias can be addressed by adjusting network structure, training for longer, using advanced optimization algorithms, or exploring alternative network architectures.
 ✋ High variance can be mitigated by getting more data, using regularization techniques, or exploring alternative network architectures.
 🔨 Deep learning has provided tools to reduce bias and variance without necessarily increasing the other.
 🙂 Regularization is a useful technique for reducing variance, although it may slightly increase bias.
 😃 Training a bigger network and getting more data usually helps reduce bias and variance, without hurting the other significantly.
 ❓ The choice of solutions depends on the specific bias or variance problem in the algorithm.
Transcript
in a previous video you saw how looking at training error and Deborah can help you diagnose whether your algorithm has a bias or variance problem or maybe both it turns out that this information that lets you much more systematically using what I call a basic recipe for machine learning that lets you much more systematically go about improving your... Read More
Questions & Answers
Q: How can you determine if your algorithm has high bias?
To determine if your algorithm has high bias, you can evaluate its performance on the training set. If it is not fitting the training set well, you may have high bias.
Q: What are some possible solutions for high bias?
Some possible solutions for high bias include adjusting network structure (adding layers or hidden units), training for longer, trying advanced optimization algorithms, or exploring alternative network architectures.
Q: How can you evaluate if your algorithm has a variance problem?
To evaluate if your algorithm has a variance problem, you can look at its performance on the test set. If it fails to generalize well from the training set to the test set, you may have high variance.
Q: What are the recommended solutions for high variance?
The best way to solve a high variance problem is to get more data, as it helps in generalization. Regularization techniques can also be used to reduce overfitting. Exploring alternative network architectures may also help reduce variance.
Summary & Key Takeaways

The video discusses a basic recipe for diagnosing and improving bias and variance problems in machine learning algorithms.

To address high bias, possible solutions include adjusting network structure, training for longer, using advanced optimization algorithms, or exploring alternative network architectures.

To address high variance, getting more data and regularization techniques can be helpful.