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Trigger Word DetectionWelcome to the final programming assignment of this specialization!In this week’s videos, you learned about applying deep learning to speech recognition. In this assignment, you will construct a speech dataset and implement an algor

Neural Machine TranslationWelcome to your first programming assignment for this week!You will build a Neural Machine Translation (NMT) model to translate human readable dates (“25th of June, 2009”) into machine readable dates (“2009-06-25”). You will do

(1)Consider using this encoder-decoder model for machine translation.This model is a “conditional language model” in the sense that the encoder portion (shown in green) is modeling the probability of the input sentence x.[A]True[B]False答案：B解析：输入的是句子x

Emojify!Welcome to the second assignment of Week 2. You are going to use word vector representations to build an Emojifier.Have you ever wanted to make your text messages more expressive? Your emojifier app will help you do that. So rather than writing “

Operations on word vectorsWelcome to your first assignment of this week!Because word embeddings are very computionally expensive to train, most ML practitioners will load a pre-trained set of embeddings.After this assignment you will be able to:Load p

(1)Suppose you learn a word embedding for a vocabulary of 10000 words. Then the embedding vectors should be 10000 dimensional, so as to capture the full range of variation and meaning in those words.[A]True[B]False答案：B解析：注意和one-hot的区别。(2)What is t-SN

Improvise a Jazz Solo with an LSTM NetworkWelcome to your final programming assignment of this week! In this notebook, you will implement a model that uses an LSTM to generate music. You will even be able to listen to your own music at the end of the assi

Character level language model - Dinosaurus landWelcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology researchers are creating new breeds of dinosa

Building your Recurrent Neural Network - Step by StepWelcome to Course 5’s first assignment! In this assignment, you will implement your first Recurrent Neural Network in numpy.Recurrent Neural Networks (RNN) are very effective for Natural Language Proce

(1)Suppose your training examples are sentences (sequences of words). Which of the following refers to the jth word in the ith training example?[A] x(i)<j>x^{(i)<j>}x(i)<j>[B] x<i>(j)x^{<i>(j)}x<i>(j)[C] x(j)<i>

Deep Learning & Art: Neural Style TransferWelcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576).In this assi

Face Recognition for the Happy HouseWelcome to the first assignment of week 4! Here you will build a face recognition system. Many of the ideas presented here are from FaceNet. In lecture, we also talked about DeepFace.Face recognition problems commonly

(1)Face verification requires comparing a new picture against one person’s face, whereas face recognition requires comparing a new picture against K person’s faces.[A]True[B]False答案：A解析：人脸验证是验证是不是这个人；人脸识别是识别是哪个人。(2)Why do we learn a function d(img1,

keras版本链接导包import torchfrom torch import nnfrom torch import optimimport torch.nn.functional as Ffrom torch.utils.data import Datasetfrom torch.utils.data import DataLoaderfrom resnets_utils import *Dataset类class MyDataset(Dataset): def __i

keras版本链接导包import torchfrom torch import nnfrom torch import optimfrom torch.utils.data import Datasetfrom torch.utils.data import DataLoaderimport torch.nn.functional as Ffrom kt_utils import *Dataset类用于分成不同的mini-batchclass MyDataset(Dataset):

Autonomous driving - Car detectionWelcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https:

(1)You are building a 3-class object classification and localization algorithm. the classes are: pedestrian (c=1), car (c=2), motorcycle (c=3). What would be the label for the following image? Recall y=[pc,bx,by,bh,bw,c1,c2,c3]y=[p_c,b_x,b_y,b_h,b_w,c_1,c_

Residual NetworksWelcome to the second assignment of this week! You will learn how to build very deep convolutional networks, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are

Keras tutorial - the Happy HouseWelcome to the first assignment of week 2. In this assignment, you will:Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level

(1)Which of the following do you typically see as you move to deeper layer in a ConvNet?[A] nHn_HnH​ and nWn_WnW​ decrease, while nCn_CnC​ increases.[B] nHn_HnH​ and nWn_WnW​ decrease, while nCn_CnC​ also decreases.[C] nHn_HnH​ and nWn_WnW​ increases, w

TensorFlow版原文导入所需要的包import torchfrom torch import nnfrom torch import optimfrom cnn_utils import *Flatten类由于早期的PyTorch没有提供nn.Faltten 类，所以这里需要手写一个class Flatten(nn.Module): def __init__(self, start_dim=1, end_dim=-1): super(Flatten, sel

Convolutional Neural Networks: Application注：由于tensorflow版本不同，输出结果可能会不同。Welcome to Course 4’s second assignment! In this notebook, you will:Implement helper functions that you will use when implementing a TensorFlow modelImplement a fully functioning C

Convolutional Neural Networks: Step by StepWelcome to Course 4’s first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation.No

(1)What do you think applying this filter to a grayscale image will do?∣01−1013−3−113−3−101−10∣\left| \begin{matrix} 0& 1& -1& 0\\ 1& 3& -3& -1\\ 1& 3& -3& -1\\ 0& 1& -1& 0\\\end{matrix} \r

今天博主在研究卷积神经网络的反向传播算法时，产生了这么一个疑问：pooling层没有卷积核，那反向传播的时候，做了些什么呢？更新了什么参数呢? 有一位博主提到：池化层一般没有参数，所以反向传播的时候，只需对输入参数求导，不需要进行权值更新。但是具体在计算的时候是要根据Max还是Average来进行区分，进行参数更新的。 我们来看看池化层的前向传播和反向传播过程   1 Max-P...

(1) To help you practice strategies for machine learning, in this week we’ll present another scenario and ask how you would act. We think this “simulator” of working in a machine learning project will give a task of what leading a machine learning project

(1)Problem StatementThis example is adapted from a real production application, but with details disguised to protect confidentiality.You are a famous researcher in the City of Peacetopia. The people of Peacetopia have a common characteristic: they are

PyTorch参考原文TensorFlow框架1 - Exploring the PyTorch Libraryimport mathimport numpy as npimport h5pyimport matplotlib.pyplot as pltimport torchfrom torch import nnfrom torch.nn import functional as Ffrom tf_utils import load_dataset, random_mini_batc

TensorFlow TutorialWelcome to this week’s programming assignment. Until now, you’ve always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build neural networks more easily. Machine learni

(1)If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. True or False?答案：False解析：当有很多参数的时候，不知道哪个参数比较重要。课程中的例子：在

Optimization MethodsUntil now, you’ve always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that can speed up learning and perhaps even get you to a better final va

(1)Which notation would you use to denote the 3rd layer’s activations when the input is the 7th example from the 8th mini-batch[A] a[3]{7}(8)a^{[3]\{7\}(8)}a[3]{7}(8)[B] a[8]{7}(3)a^{[8]\{7\}(3)}a[8]{7}(3)[C] a[8]{3}(7)a^{[8]\{3\}(7)}a[8]{3}(7)[D] a[3]

Gradient CheckingWelcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.You are part of a team working to make mobile payments available globally, and are asked to build a deep learning mod

RegularizationWelcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough. Sure it does well on the training set, but the le

InitializationWelcome to the first assignment of “Improving Deep Neural Networks”.Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.If you completed the previous cour

(1)If you have 10,000,000 examples, how would you split the train/dev/test set?[A] 98% train. 1% dev. 1%test[B] 33% train. 33% dev. 33%test[A] 60% train. 20% dev. 20%test答案：A解析：见视频1.1 Train/dev/test sets. (2)The dev and test set should:[A]Come from

Deep Neural Network for Image Classification: ApplicationWhen you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course!You will use the functions you’d implemented in the p

Building your Deep Neural Network: Step by StepWelcome 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

(1)What is the “cache” used for in our implementation of forward propagation and backward propagation?[A]It is used to keep track of the hyperparameters that we are searching over, to speed up computation.[B]We use it to pass variables computed during fo