材料 Materials
- 蛋 4 顆（4 eggs）
- 油 55g（55g oil）
- 低筋麵粉 73g（73g pastry flour）
- 牛奶 55g（55g milk）
- 醋 7g（7g vinegar）
- 糖 55g（55g sugar）
這樣的份量是一個 6 英吋中空圓形烤模。參考資料影片為 7 寸中空模。
Run Less Run Faster
3 + 2 Training
3 - Three quality runs
Track repeats
Tempo
Long
- repeat: overload - fatigue - recovery - adaption
- overload means: father, faster, more often. Overload ONLY ONE variable at a time.
2 - Two cross training
Notes
- Consistency is the key to fitness.
- Ideal racing temperatures: 40 to 60 degrees Fahrenheit.(5 to 16 degrees Celsius.)
- If degree is above 60F, -1sec/mile/degree(F).
CS230 Deep Learning
We use www.menti.com to communicate with teacher
Steps of ML application
1. Select problem
2. Get data
How long do you spend on collecting data?
It’s recommend that quickly collect data at the firt time as we start the project, so we can modify the design of the model or collect more data by training the model.
Keep clear notes on reperlment line(?).
How would you collecting data?
3. Design model
4. Train model
5. Test model
6. Deploy
Edge devices
audio —> NN —-> 0/1
but their might be large NN, it waste times.
audio —> VAD —> NN —> 0/1
VAD is checking if the voice actually detective.
option 1 will be more simpler and faster to option 2. It maybe have many errors or noises in the result of option 1, but the following large NN will take care of it, we don’t have to worry about it.
In this case, option 2 will be a proper choice for Non-ML just detect for loud or seilence, it can not distinguish the anscent.
7. Maintain
TOEFL in Biology
Optimize Backpropagation of Cost Function in Neural Network (CS229)
Idea of backpropagation
Backpropagation Intuition of Neural Network (CS229)
Recall cost function
$$J(\Theta)=-\frac{1}{m}\left[\sum_{i=1}^m\sum_{k=1}^Ky_k^{(i)}\log\left(h_{\Theta}(x^{(i)})\right)_k+(1-y_k^{(i)})\log\left(1-h_{\theta}(x^{(i)})_k\right)\right]+\frac{\lambda}{2m}\sum_{l=1}^{L-1}\sum_{i=1}^{s_l}\sum_{j=1}^{s_l+1}\left(\Theta_{ji}^{(l)}\right)^2\tag{1}$$
Backpropagation Algorithm for Minimize Cost Function (CS229)
Recall cost function in neural network
$$J(\Theta)=-\frac{1}{m}\left[\sum_{i=1}^m\sum_{k=1}^Ky_k^{(i)}\log\left(h_{\Theta}(x^{(i)})\right)_k+(1-y_k^{(i)})\log\left(1-h_{\theta}(x^{(i)})_k\right)\right]+\frac{\lambda}{2m}\sum_{l=1}^{L-1}\sum_{i=1}^{s_l}\sum_{j=1}^{s_l+1}\left(\Theta_{ji}^{(l)}\right)^2$$