Practice Building and Training Simple MLPs with TensorFlow/Keras - 11.6.3 | Module 6: Introduction to Deep Learning (Weeks 11) | Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skillsβ€”perfect for learners of all ages.

games

11.6.3 - Building and Training Simple MLPs with TensorFlow/Keras

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What libraries do we import in TensorFlow for building MLPs?

πŸ’‘ Hint: Think about the components used in model building.

Question 2

Easy

What does Dense refer to in Keras?

πŸ’‘ Hint: Consider how neurons are structured within a layer.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is Keras?

  • A type of neural network
  • A high-level API for neural networks
  • A machine learning dataset

πŸ’‘ Hint: It is commonly used with TensorFlow.

Question 2

True or False: An epoch is a single pass through the training data.

  • True
  • False

πŸ’‘ Hint: Consider how many times the model sees the data.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Describe a situation where you would prefer using Keras's Functional API over the Sequential API. What advantages does it offer?

πŸ’‘ Hint: Think about scenarios that involve branching or merging models.

Question 2

Given a dataset with both structured and unstructured data, outline the steps you would take to prepare, build, and train a MLP model using TensorFlow/Keras.

πŸ’‘ Hint: Consider how mixed data types might influence feature engineering.

Challenge and get performance evaluation