Fundamentals of Neural Networks - Lab 3 - Deep CNN

Fundamentals of Neural Networks - Lab 3 - Deep CNN

Assessment

Interactive Video

Computers

9th - 10th Grade

Hard

Created by

Wayground Content

FREE Resource

The video tutorial covers the construction and evaluation of a deep convolutional neural network (CNN) using TensorFlow. It begins with an introduction to the CNN architecture and the Fashion MNIST dataset, which consists of 70,000 grayscale images across 10 categories. The tutorial then explains data preprocessing, including rescaling and visualization. The CNN model is built using TensorFlow's sequential API, and the importance of using the functional API for flexibility is highlighted. The model is trained and evaluated, achieving a 92% accuracy on the training set and 90% on the test set. The tutorial concludes with a discussion on prediction accuracy and potential research questions.

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10 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the main focus of today's lab session?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain the significance of the number of trainable parameters in a model.

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe the Fashion MNIST dataset used in the lab.

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

What are the dimensions of each image in the dataset?

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5.

OPEN ENDED QUESTION

3 mins • 1 pt

What are the different classes present in the Fashion MNIST dataset?

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6.

OPEN ENDED QUESTION

3 mins • 1 pt

Discuss the importance of rescaling numerical inputs in the model.

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7.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the difference between the Sequential API and the Functional API in TensorFlow?

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