Neural Networks and Deep Learning Quiz Answer
In this article i am gone to share a Coursera Course: Neural Networks and Deep Learning Quiz Answer with you..
About this Course
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.
SKILLS YOU WILL GAIN
- Deep Learning
- Artificial Neural Network
- Backpropagation
- Python Programming
- Neural Network Architecture
Go to this Course: https://www.coursera.org/learn/neural-networks-deep-learning
Introduction to Deep Learning Week 1 Quiz Answer
Question 1) What does the analogy “AI is the new electricity” refer to?
- Through the “smart grid”, AI is delivering a new wave of electricity.
- AI is powering personal devices in our homes and offices, similar to electricity.
- Similar to electricity starting about 100 years ago, AI is transforming multiple industries.
- AI runs on computers and is thus powered by electricity, but it is letting computers do things not possible before.
Question 2) Which of these are reasons for Deep Learning recently taking off? (Check the two options that apply.)
- We have access to a lot more data.
- Neural Networks are a brand new field.
- We have access to a lot more computational power.
- Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.
Question 3) Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)
- It is faster to train on a big dataset than a small dataset.
- Faster computation can help speed up how long a team takes to iterate to a good idea.
- Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.
- Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).
Question 4) When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?
- True
- False
Question 5) Which one of these plots represents a ReLU activation function?
Question 6) Images for cat recognition is an example of “structured” data, because it is represented as a structured array in a computer. True/False?
- True
- False
Question 7) A demographic dataset with statistics on different cities’ population, GDP per capita, economic growth is an example of “unstructured” data because it contains data coming from different sources. True/False?
- True
- False
Question 8) Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)
- It can be trained as a supervised learning problem.
- It is applicable when the input/output is a sequence (e.g., a sequence of words).
- It is strictly more powerful than a Convolutional Neural Network (CNN).
- RNNs represent the recurrent process of Idea->Code->Experiment->Idea->….
Question 9) In this diagram which we hand-drew in lecture, what do the horizontal axis (x-axis) and vertical axis (y-axis) represent?
- x-axis is the amount of data
- y-axis (vertical axis) is the performance of the algorithm.
Question 10) Assuming the trends described in the previous question’s figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)
- Increasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
- Decreasing the training set size generally does not hurt an algorithm’s performance, and it may help significantly.
- Increasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.
- Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly.