Days 11-12 of Andrew Ng's Supervised Machine Learning course: overfitting vs underfitting, and fixing them with more data, simpler models or regularization.
Today is mainly learning about “Decision boundary”, “Cost function of logistic regresion”, “Logistic loss” and “Gradient Descent Implementation for logistic regression”.
Oh boy… I was sick for almost two weeks 🤒 After a brief break, I’m back to dive deep into machine learning, and today, we’ll revisit one of the core concepts in training models—gradient descent.…
Today I started with Choosing the learning rate, reviewed the Jupyter lab and learnt what is feature engineering.
Day 7 of Andrew Ng's Supervised Machine Learning course: why feature scaling matters and how to check that gradient descent is converging.
Today I spent 10 30 60 mins reviewing previous notes, just realized that’s a lot.
Day 4 was a long day for me, just got 15mins before bed to quickly skim through the video “multiple features” and “vectorization part 1”.
Day 2 I was busy and managed only 15 mins for “Supervised Machine Learning” video + 15 mins watched But what is a neural network? | Chapter 1, Deep learning from 3blue1brown.