求英文歌,女高音第一句in the ocean deep dawn,高潮是男声合唱

您好：不清楚，但是好听的英文歌好多呢~~~本人学英语的推荐这些吧希望你能喜欢。

1、Bubbly--Colbie Caillat（你听过一遍就会非常喜欢的歌）；

　　2、Burning--Maria Arredondo；

　　3、Happy--丽安娜 刘易斯；

　　4、Cry On My Shoulder--出自德国选秀节目（很早的一首，非常好听）；

　　5、Apologize--Timbaland；

　　6、The Climb--Miley Cyrus（个人最喜欢的歌手之一）；

　　7、You Belong With Me--泰勒斯威夫特（绝棒的）；

　　8、I Stay In Love--玛利亚凯莉；

　　9、I Didn't Know My Own Strength--Whitney Houston（是非常棒的一首慢歌，也是我非常喜欢的黑人歌手之一。）；

　　10、A Little Bit Longer--Jonas Brothers（嗓音非常棒的组合，几乎每首都很好听，尤其是这首！强力推荐）；

　　11、The Little Things--Colbie Caillat；

　　12、Mad--尼欧；

　　13、My All--玛丽亚凯莉（据说非常适合作手机铃声的歌）；

　　14、My Love--WestLife（西域成名金曲，经典老歌，诠释了所有经典的定义。）；

　　15、Need You Now--Lady Antebellum（时下排行榜热门歌曲）；

　　16、The Saltwater Room--Owl City（最爱的歌手之一，曲风相当特别）；

　　17、Take A Bow--Rihanna（听2秒就会爱上的歌手和歌~）；

　　18、The Technicolor Phase--Owl City（《爱丽丝梦游仙境》的主题曲之一）；

　　19、This Is It--迈克尔杰克逊（不知到底是翻唱还是遗作，都能再现天王的独特魅力）；

　　20、Who Says--John Mayer（类似乡村风以吉他为伴奏，这首非常棒！）；

　　21、Just One Last Dance--Sarah Connor（这个经典的不用说吧，）；

　　22、Angle--Sarah Mclachlan（天籁之音~~）；23、Living To Love You--Sarah Connor（歌词催人泪下，我最喜欢的慢歌之一）；

　　24、Nothings Gonna Change My Love For You--Glenn Mediros（被方大同翻唱过，那肯定好听拉）；

　　25、I Look To You--Whitney Houston；

　　26、I Got You--丽安娜刘易斯；

　　27、Love To Be Loved By You--马克特伦茨（歌词和曲调非常感人！）；

　　28、Butterfly Fly Away--Miley Cyrus（《乖乖女是大明星》的插曲，讲的父亲对女儿的爱的故事，曲风清新）；

　　29、Eversleeping--Xandria（不会让你失望的慢歌）；

　　30、Wonderful Tonight--Baby Face（也是被方大同翻唱的歌）；

　　31、Still Crazy In Love--Sarah Connor；

　　32、We Can Work It Out --Sweetbox；

　　33、Sexy Love--Ne Yo；

　　34、Happily Never After--Pussycat Dolls；

　　35、A Fine Frenzy--Almost Lover（后面的才是歌名，曲调有点小特别~）；

　　36、Craigie hill----Cara Dillon（首推这首，温馨极了，好听极了。有点像m2m的声音。） ；

　　37、Down by the Sally Gardens（歌手不明，但是爱尔兰的风笛爆好听娓娓的旋律，背景音乐也很好听） ；

　　38、Beautiful Boy--Celine Dion（歌手不用介绍）；

　　39、A Place Nearby与Unforgivable Sinner--Lene Marlin（挪威创作才女，） ；

　　40、Scarborough Fair(毕业生)：（《Scarborough Fair》是美国六十年代最受大学生欢迎的**、1968年奥斯卡获奖片《毕业生》（达斯汀·霍夫曼主演，其成名作）中的主题曲。本人还是喜欢布莱曼她唱的。 ）；

　　41、classicriver：（第一次听这曲子的时候是在初秋的深夜,偶然听到了它,刹时间时间和空间好象都凝固了一样!听着它,感觉深藏心底的那份无尽地孤独被慢慢地勾起, 曾经的回忆, 失去的快乐，刻骨的伤心，和短暂拥有，都在那一刻漂浮了起来,占据了身边的所有的空间 它让我感觉到了这世间最珍贵的是亲情，爱情．金钱算得了什么呢）． ＜classicriver＞很多人都听过的旋律．这样的经典歌曲是无价之宝．相信当你知道这歌曲之后，如果突然失去它，你会觉得好孤独，无助．．． 这样的音乐是无价之宝～）；

　　42、If I Were A Boy--Beyonce（可以做铃声！开头就已经把气氛带起来了~）；

　　43、Love You Lately--Daniel Powter；

　　44、I Hate Love--Claude Kelly；

　　45、Amarantine--Enya（节奏非常棒的一首天籁，经典慢歌～）；

　　46、Better In Time--Leona Lewis；

　　47、Crush--David Archuleta；

　　48、You Raise Me Up--Westlife；

　　49、Realize--Colbie Caillat（科比凯拉，几乎她的每一首歌都是那么的特别和好听。非常喜欢的歌手之一）；

　　50、I See You--Leona Lewis（就是《阿凡达》的主题曲，看过**再听这个歌，我们能听到的，就不止是幻想与憧憬了，还有爱和感动）；

　　51、Day Too Soon--Sia（也是个所有歌几乎都不错的歌手。）；

　　52、Doesn't Mean Anything--Alicia Keys（个人钟爱的一首歌！非常非常好听！）；

　　53、It's Amazing--Jem（节奏非常好！><，不会后悔的歌哦~）；

　　54、Lovebug--Jonas Brothers（高潮非常明快，清新，非常舒服的一首小情歌~喜欢啊～）；

　　55、When You're Mad--Ne-Yo（尼欧的歌总是那么那么好听，不管是RAP风还是R&B，都非常棒！）；

　　56、One Fine Wire--Colbie Caillat（高潮曲调的设计有点小俏皮。）；

　　57、Vidas Paralelas--Ximena Sarinana（一首法语歌，节奏明快。个人觉得偶尔听听法语歌也是满有趣的。笑~）；

　　58、Wait Til You Here From You--Sarah Connor（开头的独白，那种声音令人放松，接着的曲调非常的好听！推荐！）；

　　59、Sitting Down Here--琳恩玛莲（开头就足以让你喜欢的调调～高潮适合作铃声。）；

　　60、A Place Nearby--琳恩玛莲（全曲以纯明的钢琴和鼓点贯穿。曲调单纯，听了叫人放松。）；

　　61、When You Believe--Mariah Carey＆Whitney Houston（两个天后的合音，完美中的完美啊！巨好听！）；

　　62、Dilemma--Kelly Rowland（非常非常好听！高潮部分非常非常适合做铃声！女生手机必备！）；

　　63、No Air--约尔丁斯巴克斯（开头足以定风格，可以作铃声）；

　　64、The Best Day--Taylar Swift；

　　65、Viva La Vida--Coldplay；

　　66、Wait For You--Elliott Yamin（非常非常非常好听的！曾经就听过，昨天才终于被我找到~）；

　　67、Time For Miracles--Harald Kloser；

　　68、When I'm With You--西城男孩（又一首经典旧歌。真的是，开场就征服了我，）；

　　69、A Todo Color--魏如萱（西班牙语，有点甜美的意味。）；

　　70、I Ain't Tryin'--KeAnthong；

　　71、Buttons--Sia（曲调小特别～）

　　72、Little Bit Better--玛丽亚亚瑞唐多（听这首歌的开头，心里就留下一句话：这歌怎么这么好听啊，笑~）；

　　73、Trip Around The World--Alexz Johnson（可作铃声，女生版清新说唱风，带给你不一样的感觉。）；

　　74、Gonna Get It--Alexz Johnson（开头的尖叫够震撼，够特别，可作铃声）；

　　75、Can Anybody Hear Me--Meredith Andrews（嗓音听起来很舒服，比较喜欢高潮以外的部分。）；

　　76、Eh Eh(Nothing Eale I Can Say--Lady GaGa（GaGa的新歌，开头很特别，可作铃声。感觉风格有点像玛利亚凯莉～）；

　　77、Before The Down--Jennifer Rush（有点怀旧的感觉。）；

　　78、As Long As It Takes--Meredith Andrews（很纯的嗓音）；

　　79、Stupid In Love--Rihanna（开头的鼓点不错，R的嗓音特别吸引人，但个人还是更喜欢她的快歌）；

　　80、Give You Hell--The All-American Rejects（可爱的背景音乐，很欢快的节奏，不过歌手是男的哟~）；

　　81、Welcome To My Life--Simple Plan（这首要个人慢慢去体味，可能第一遍会觉得一般般，但其实后来会觉得蛮有味道的。）我几乎不听中文歌，所以有很多英文歌都很好听啊。

望采纳~~祝好~~~~

Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer) This week, you will build a deep neural network, with as many layers as you want!

After this assignment you will be able to:

Notation :

Let's get started!

Let's first import all the packages that you will need during this assignment

To build your neural network, you will be implementing several "helper functions" These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps Here is an outline of this assignment, you will:


<caption><center> Figure 1 </center></caption>

Note that for every forward function, there is a corresponding backward function That is why at every step of your forward module you will be storing some values in a cache The cached values are useful for computing gradients In the backpropagation module you will then use the cache to calculate the gradients This assignment will show you exactly how to carry out each of these steps

You will write two helper functions that will initialize the parameters for your model The first function will be used to initialize parameters for a two layer model The second one will generalize this initialization process to $L$ layers

Exercise : Create and initialize the parameters of the 2-layer neural network

Instructions :

Expected output :

<table style="width:80%">

<tr>

<td> W1 </td>

<td> [[ 001624345 -000611756 -000528172]

[-001072969 000865408 -002301539]] </td>

</tr>

<tr>

<td> b1 </td>

<td>[[ 0]

[ 0]]</td>

</tr>

<tr>

<td> W2 </td>

<td> [[ 001744812 -000761207]]</td>

</tr>

<tr>

<td> b2 </td>

<td> [[ 0]] </td>

</tr>

</table>

The initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and bias vectors When completing the initialize_parameters_deep , you should make sure that your dimensions match between each layer Recall that $n^{[l]}$ is the number of units in layer $l$ Thus for example if the size of our input $X$ is $(12288, 209)$ (with $m=209$ examples) then:

<table style="width:100%">

<tr>

<td> Layer L-1 </td>

<td> $(n^{[L-1]}, n^{[L-2]})$ </td>

<td> $(n^{[L-1]}, 1)$ </td>

<td>$Z^{[L-1]} = W^{[L-1]} A^{[L-2]} + b^{[L-1]}$ </td>

<td> $(n^{[L-1]}, 209)$ </td>

<tr>

<tr>

<td> Layer L </td>

<td> $(n^{[L]}, n^{[L-1]})$ </td>

<td> $(n^{[L]}, 1)$ </td>

<td> $Z^{[L]} = W^{[L]} A^{[L-1]} + b^{[L]}$</td>

<td> $(n^{[L]}, 209)$ </td>

<tr>

</table>

Remember that when we compute $W X + b$ in python, it carries out broadcasting For example, if:

$$ W = \begin{bmatrix}

j & k & l\

m & n & o \

p & q & r

\end{bmatrix};;; X = \begin{bmatrix}

a & b & c\

d & e & f \

g & h & i

\end{bmatrix} ;;; b =\begin{bmatrix}

s \

t \

u

\end{bmatrix}\tag{2}$$

Then $WX + b$ will be:

$$ WX + b = \begin{bmatrix}

(ja + kd + lg) + s & (jb + ke + lh) + s & (jc + kf + li)+ s\

(ma + nd + og) + t & (mb + ne + oh) + t & (mc + nf + oi) + t\

(pa + qd + rg) + u & (pb + qe + rh) + u & (pc + qf + ri)+ u

\end{bmatrix}\tag{3} $$

Exercise : Implement initialization for an L-layer Neural Network

Instructions :

Expected output :

<table style="width:80%">

<tr>

<td> W1 </td>

<td>[[ 001788628 00043651 000096497 -001863493 -000277388]

[-000354759 -000082741 -000627001 -000043818 -000477218]

[-001313865 000884622 000881318 001709573 000050034]

[-000404677 -00054536 -001546477 000982367 -001101068]]</td>

</tr>

<tr>

<td> b1 </td>

<td>[[ 0]

[ 0]

[ 0]

[ 0]]</td>

</tr>

<tr>

<td> W2 </td>

<td>[[-001185047 -00020565 001486148 000236716]

[-001023785 -000712993 000625245 -000160513]

[-000768836 -000230031 000745056 001976111]]</td>

</tr>

<tr>

<td> b2 </td>

<td>[[ 0]

[ 0]

[ 0]]</td>

</tr>

</table>

Now that you have initialized your parameters, you will do the forward propagation module You will start by implementing some basic functions that you will use later when implementing the model You will complete three functions in this order:

The linear forward module (vectorized over all the examples) computes the following equations:

$$Z^{[l]} = W {[l]}A {[l-1]} +b^{[l]}\tag{4}$$

where $A^{[0]} = X$

Exercise : Build the linear part of forward propagation

Reminder :

The mathematical representation of this unit is $Z^{[l]} = W {[l]}A {[l-1]} +b^{[l]}$ You may also find npdot() useful If your dimensions don't match, printing Wshape may help

Expected output :

<table style="width:35%">

<tr>

<td> Z </td>

<td> [[ 326295337 -123429987]] </td>

</tr>

</table>

In this notebook, you will use two activation functions:

For more convenience, you are going to group two functions (Linear and Activation) into one function (LINEAR->ACTIVATION) Hence, you will implement a function that does the LINEAR forward step followed by an ACTIVATION forward step

Exercise : Implement the forward propagation of the LINEAR->ACTIVATION layer Mathematical relation is: $A^{[l]} = g(Z^{[l]}) = g(W {[l]}A {[l-1]} +b^{[l]})$ where the activation "g" can be sigmoid() or relu() Use linear_forward() and the correct activation function

Expected output :

<table style="width:35%">

<tr>

<td> With sigmoid: A </td>

<td > [[ 096890023 011013289]]</td>

</tr>

<tr>

<td> With ReLU: A </td>

<td > [[ 343896131 0 ]]</td>

</tr>

</table>

Note : In deep learning, the "[LINEAR->ACTIVATION]" computation is counted as a single layer in the neural network, not two layers

For even more convenience when implementing the $L$-layer Neural Net, you will need a function that replicates the previous one ( linear_activation_forward with RELU) $L-1$ times, then follows that with one linear_activation_forward with SIGMOID

Exercise : Implement the forward propagation of the above model

Instruction : In the code below, the variable AL will denote $A^{[L]} = \sigma(Z^{[L]}) = \sigma(W^{[L]} A^{[L-1]} + b^{[L]})$ (This is sometimes also called Yhat , ie, this is $\hat{Y}$)

Tips :

<table style="width:40%">

<tr>

<td> AL </td>

<td > [[ 017007265 02524272 ]]</td>

</tr>

<tr>

<td> Length of caches list </td>

<td > 2</td>

</tr>

</table>

Great! Now you have a full forward propagation that takes the input X and outputs a row vector $A^{[L]}$ containing your predictions It also records all intermediate values in "caches" Using $A^{[L]}$, you can compute the cost of your predictions

Now you will implement forward and backward propagation You need to compute the cost, because you want to check if your model is actually learning

Exercise : Compute the cross-entropy cost $J$, using the following formula: $$-\frac{1}{m} \sum\limits_{i = 1}^{m} (y {(i)}\log\left(a {[L] (i)}\right) + (1-y^{(i)})\log\left(1- a^{ L }\right)) \tag{7}$$

Expected Output :

<table>

</table>

Just like with forward propagation, you will implement helper functions for backpropagation Remember that back propagation is used to calculate the gradient of the loss function with respect to the parameters

Reminder :

Now, similar to forward propagation, you are going to build the backward propagation in three steps:

For layer $l$, the linear part is: $Z^{[l]} = W^{[l]} A^{[l-1]} + b^{[l]}$ (followed by an activation)

Suppose you have already calculated the derivative $dZ^{[l]} = \frac{\partial \mathcal{L} }{\partial Z^{[l]}}$ You want to get $(dW^{[l]}, db^{[l]} dA^{[l-1]})$

The three outputs $(dW^{[l]}, db^{[l]}, dA^{[l]})$ are computed using the input $dZ^{[l]}$Here are the formulas you need:

$$ dW^{[l]} = \frac{\partial \mathcal{L} }{\partial W^{[l]}} = \frac{1}{m} dZ^{[l]} A^{[l-1] T} \tag{8}$$

$$ db^{[l]} = \frac{\partial \mathcal{L} }{\partial b^{[l]}} = \frac{1}{m} \sum_{i = 1}^{m} dZ^{ l }\tag{9}$$

$$ dA^{[l-1]} = \frac{\partial \mathcal{L} }{\partial A^{[l-1]}} = W^{[l] T} dZ^{[l]} \tag{10}$$

Exercise : Use the 3 formulas above to implement linear_backward()

Expected Output :

<table style="width:90%">

<tr>

<td> dA_prev </td>

<td > [[ 051822968 -019517421]

[-040506361 015255393]

[ 237496825 -089445391]] </td>

</tr>

</table>

Next, you will create a function that merges the two helper functions: linear_backward and the backward step for the activation linear_activation_backward

To help you implement linear_activation_backward , we provided two backward functions:

If $g()$ is the activation function,

sigmoid_backward and relu_backward compute $$dZ^{[l]} = dA^{[l]} g'(Z^{[l]}) \tag{11}$$

Exercise : Implement the backpropagation for the LINEAR->ACTIVATION layer

Expected output with sigmoid:

<table style="width:100%">

<tr>

<td > dA_prev </td>

<td >[[ 011017994 001105339]

[ 009466817 000949723]

[-005743092 -000576154]] </td>

</tr>

</tr>

</tr>

</table>

Expected output with relu

<table style="width:100%">

<tr>

<td > dA_prev </td>

<td > [[ 044090989 0 ]

[ 037883606 0 ]

[-02298228 0 ]] </td>

</tr>

</tr>

</tr>

</table>

Now you will implement the backward function for the whole network Recall that when you implemented the L_model_forward function, at each iteration, you stored a cache which contains (X,W,b, and z) In the back propagation module, you will use those variables to compute the gradients Therefore, in the L_model_backward function, you will iterate through all the hidden layers backward, starting from layer $L$ On each step, you will use the cached values for layer $l$ to backpropagate through layer $l$ Figure 5 below shows the backward pass

Initializing backpropagation:

To backpropagate through this network, we know that the output is,

$A^{[L]} = \sigma(Z^{[L]})$ Your code thus needs to compute dAL $= \frac{\partial \mathcal{L}}{\partial A^{[L]}}$

To do so, use this formula (derived using calculus which you don't need in-depth knowledge of):

You can then use this post-activation gradient dAL to keep going backward As seen in Figure 5, you can now feed in dAL into the LINEAR->SIGMOID backward function you implemented (which will use the cached values stored by the L_model_forward function) After that, you will have to use a for loop to iterate through all the other layers using the LINEAR->RELU backward function You should store each dA, dW, and db in the grads dictionary To do so, use this formula :

$$grads["dW" + str(l)] = dW^{[l]}\tag{15} $$

For example, for $l=3$ this would store $dW^{[l]}$ in grads["dW3"]

Exercise : Implement backpropagation for the [LINEAR->RELU] $\times$ (L-1) -> LINEAR -> SIGMOID model

Expected Output

<table style="width:60%">

<tr>

<td > dW1 </td>

<td > [[ 041010002 007807203 013798444 010502167]

[ 0 0 0 0 ]

[ 005283652 001005865 001777766 00135308 ]] </td>

</tr>

[ 0 ]

[-002835349]] </td>

</tr>

<tr>

<td > dA1 </td>

<td > [[ 0 052257901]

[ 0 -03269206 ]

[ 0 -032070404]

[ 0 -074079187]] </td>

</tr>

</table>

In this section you will update the parameters of the model, using gradient descent:

$$ W^{[l]} = W^{[l]} - \alpha \text{ } dW^{[l]} \tag{16}$$

$$ b^{[l]} = b^{[l]} - \alpha \text{ } db^{[l]} \tag{17}$$

where $\alpha$ is the learning rate After computing the updated parameters, store them in the parameters dictionary

Exercise : Implement update_parameters() to update your parameters using gradient descent

Instructions :

Update parameters using gradient descent on every $W^{[l]}$ and $b^{[l]}$ for $l = 1, 2, , L$

Deep: 深的；深远的

比较级：deeper最高级：deepest

Further: 更远的，较远的；更进一步的，深一层的；更多的

选further因为星夜是用更远来表达的，不是用深来表达