## Projects

# Deep Learning and Machine Learning Notes

## Courses

### MIT Deep Learning
- [reference](http://introtodeeplearning.com/)

## Linear Regression

## RNN

### Backpropagation

### LSTM
- [reference](https://tengyuanchang.medium.com/%E6%B7%BA%E8%AB%87%E9%81%9E%E6%AD%B8%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF-rnn-%E8%88%87%E9%95%B7%E7%9F%AD%E6%9C%9F%E8%A8%98%E6%86%B6%E6%A8%A1%E5%9E%8B-lstm-300cbe5efcc3)
- [reference](https://brohrer.mcknote.com/zh-Hant/how_machine_learning_works/how_rnns_lstm_work.html

1. Maintain a cell state
2. Use gates to control

## Projects

No, the classifier for a machine learning model, such as XGBoost, is typically part of **supervised learning**, not unsupervised learning. Here’s a brief overview to clarify:

### Supervised Learning
- **Definition:** Involves training a model on a labeled dataset, where the outcome or target variable is known.
- **Examples:** Classification (e.g., XGBoost classifier), Regression.
- **Algorithms:** XGBoost, Random Forest, SVM, Neural Networks, etc.

### Unsupervised Learning
- **Definition:** Involves training a model on data without labeled outcomes, aiming to find hidden patterns or intrinsic structures.
- **Examples:** Clustering, Dimensionality Reduction, Anomaly Detection.
- **Algorithms:** K-Means, PCA, Autoencoders, etc.

### XGBoost
- **Category:** Supervised Learning.
- **Use Cases:** Can be used for classification (e.g., predicting categories) and regression (e.g., predicting continuous values).

In your case, since you're dealing with a classifier and predicting temperature adjustments, you are working with a supervised learning approach.