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# System prepended metadata

title: Foundation for Image Classification

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# Foundation for Image Classification
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### 1. Machine learning

#### 1.1. Linear Regression:

##### 1.1.1. Definition:
* What is **Linear Regression**:
    * Define by word.
    * Define by mathematics format:
        * Dataset:
    * Hàm Loss:
        * de danh gia mo hinh
   
        * Ham loss anh huong gi den mo hinh
    * Thuật toán:
            * Tính đạo hàm theo W, b
                * Cập nhật:

##### 1.1.2. Loss function:
* What is it?
* Types of loss function in Linear Regression:
    * Mean Square Error(MSE):
        * its formula
        * its derivative
    * Mean Absolute Error(MAE):
        * its formula
        * its derivative

##### 1.1.3. How to solve Linear Regression problem:
* Directly: solve by inverse matrix / pseudo-inverse matrix
* Discrete process: use Gradient descend(GD) ...

##### 1.1.4. Gradient descend:
* Intuition of GD
* What is L1/L2 - Regularization? Why we use it?
##### 1.1.5. Implement in Python
* Generate random data
* Implement GD algorithm

##### 1.1.6. Conclusion:
* What is bias in Linear Regression? Can we evaluate anything from bias?
* Advantage and disadvantage of MSE & MAE
* Why we should not use directly method to solve linear regression problem?
* When we can and when we cannot use GD?
##### 1.1.7. Reference:
* [machinelearningcoban](https://machinelearningcoban.com/2016/12/28/linearregression/)
* [GD](https://machinelearningcoban.com/2017/01/12/gradientdescent/)
* [GD2](https://machinelearningcoban.com/2017/01/16/gradientdescent2/)
* [Google.com](google.com)