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。
Marathon. Food. Programming.
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。
We use www.menti.com to communicate with teacher
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(?).
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.
$$J(\Theta)=-\frac{1}{m}\left[\sum_{i=1}^m\sum_{k=1}^Ky_k^{(i)}\log\left(h_{\Theta}(x^{(i)})\right)_k+(1-y_k^{(i)})\log\left(1-h_{\theta}(x^{(i)})_k\right)\right]+\frac{\lambda}{2m}\sum_{l=1}^{L-1}\sum_{i=1}^{s_l}\sum_{j=1}^{s_l+1}\left(\Theta_{ji}^{(l)}\right)^2\tag{1}$$
$$J(\Theta)=-\frac{1}{m}\left[\sum_{i=1}^m\sum_{k=1}^Ky_k^{(i)}\log\left(h_{\Theta}(x^{(i)})\right)_k+(1-y_k^{(i)})\log\left(1-h_{\theta}(x^{(i)})_k\right)\right]+\frac{\lambda}{2m}\sum_{l=1}^{L-1}\sum_{i=1}^{s_l}\sum_{j=1}^{s_l+1}\left(\Theta_{ji}^{(l)}\right)^2$$