Compare machine learning and deep learning in the context of formal and informal education.

Machine learning and deep learning are both subfields of artificial intelligence, but they differ in complexity and application. Machine learning focuses on algorithms that learn from data through statistical techniques and require less computational power compared to deep learning. Deep learning, a subset of machine learning, involves neural networks with many layers, requiring large datasets and significant computational resources to train. In formal education, machine learning may be introduced as a foundational concept, while deep learning is usually tackled in advanced courses. Informally, deep learning is often pursued by enthusiasts or practitioners via online tutorials and self-learning platforms.

Related Questions:

  1. Define data analytics and data science. Are they similar or different? Give a reason.
  2. Can you relate how data science is helpful in solving business problems?
  3. Database is useful in the field of data science. Defend this statement.
  4. What is meant by sources of data? Give three sources of data excluding those mentioned in the book.
  5. Differentiate between database and dataset.
  6. What is the OSI model, and how does it help in understanding the process of data communication? Explain each of the seven layers and their functions.
  7. Explain the evolution of the Internet from its origins to the modern-day global network. What major technological advancements contributed to its growth?
  8. Discuss the advantages and disadvantages of the Internet, considering factors like global connectivity, information access, privacy concerns, and digital addiction.
  9. Explore common applications of the Internet and their impact on various aspects of society, including communication, education, business, entertainment, and research.
  10. What is the major difference between solving simple problems and complex problems?
  11. Why do software designers prefer to use IPO charts?
  12. What are the methods used to design a solution?
  13. Which computational thinking technique breaks down a problem into smaller parts?
  14. Identify three computing problems from other subjects you are studying.
  15. Why do we need to think computationally?
  16. Telephone numbers usually have 9 digits. The first two digits represent the area code and remain constant. The last 7 digits represent the number and cannot begin with 0. How many different telephone numbers are possible with a given area code?
  17. There are 4 different roads from city A to city B and 2 different roads from city B to city C. Draw a map of the given situation and determine how many possible routes exist from city A to city C passing through city B.
  18. Identify whether the given problems are Decision Problems, Counting Problems, or Search Problems. Write your answer in front of each problem.
  19. A student has to take one course of physics, one of science, and one of mathematics. He may choose from 3 physics courses (P1, P2, P3), 2 science courses (S1, S2), and 2 mathematics courses (M1, M2). In how many ways can the student select the three courses?
  20. Create an IPO chart that accepts the ages of four boys and calculates their total and average age. The program should display both the total and the average age.