Introduction to Deep Learning:Neural Networks and Deep Learning(Deep Learning Specialization) Answers:2025

Question 1

Which of the following best describes the role of AI in the expression “an AI-powered society”?

✅ AI is an essential ingredient in realizing tasks, in industry and in personal life.
❌ AI controls the power grids…
❌ AI helps to create energy…

Explanation:
“AI-powered society” means AI technologies assist and enhance daily activities, industries, and decision-making, not that AI literally powers energy or electricity.

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Question 2

Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)

✅ 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.
✅ We have access to a lot more data.
❌ Neural Networks are a brand new field.

Explanation:
Neural networks have existed for decades. The recent success of deep learning comes from big data, high-performance GPUs, and practical results in real-world AI applications.

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Question 3

Recall this diagram of iterating over different ML ideas. Which of the statements below are true?

✅ Larger amounts of data allow researchers to try more ideas and then produce better algorithms in less time.
✅ Improvements in the GPU/CPU hardware enable the discovery of better Deep Learning algorithms.
✅ Better algorithms can speed up the iterative process by reducing the necessary computation time.
❌ Better algorithms allow engineers to get more data and then produce better Deep Learning models.

Explanation:
More data, faster hardware, and better algorithms accelerate experimentation. However, better algorithms don’t generate more data — they make better use of it.

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Question 4

When experienced deep learning engineers work 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.

✅ False

Explanation:
Even experts must experiment and iterate. Deep learning requires multiple rounds of tuning architecture, hyperparameters, and data processing.

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Question 5

ReLU stands for which of the following?

✅ Rectified Linear Unit

Explanation:
ReLU (Rectified Linear Unit) is an activation function defined as f(x) = max(0, x). It introduces non-linearity and improves training speed.

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Question 6

Which of the following are examples of unstructured data? (Choose all that apply.)

✅ Sound files for speech recognition.
✅ Images for bird recognition.
❌ Information about elephants’ weight, height, age…
❌ Text describing size and number of pages…

Explanation:
Unstructured data includes images, audio, and free text — data not organized in fixed fields or tables. Structured data includes spreadsheets or databases.

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Question 7

A demographic dataset with statistics on different cities’ population, GDP per capita, and economic growth is an example of “unstructured” data because it contains data coming from different sources.

✅ False

Explanation:
This dataset is structured, as it fits neatly into tables with numeric columns (population, GDP, etc.), regardless of data source.

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Question 8

Why can an RNN (Recurrent Neural Network) be used to create English captions to French movies? (Choose all that apply.)

✅ It can be trained as a supervised learning problem.
✅ The RNN is applicable since the input and output of the problem are sequences.
❌ RNNs are much more powerful than CNNs.
❌ RNN requires a small number of examples.

Explanation:
RNNs handle sequence-to-sequence problems (e.g., translation, captions). They learn from labeled examples (supervised learning).

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Question 9

In this diagram, what do the axes represent?

✅ x-axis is the amount of data, y-axis (vertical axis) is the performance of the algorithm.

Explanation:
The graph illustrates that as data increases, algorithm performance improves, showing how data size correlates with model accuracy.

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Question 10

Assuming the trends described in the figure are accurate. The performance of a NN depends only on the size of the NN.

✅ False

Explanation:
Performance depends on model size, data amount, quality of data, hyperparameters, and training methods, not just network size.

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🧾 Summary Table

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