Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: TensorFlow for classification

Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: TensorFlow for classification

Assessment

Interactive Video

•

Information Technology (IT), Architecture

•

University

•

Practice Problem

•

Hard

Created by

Wayground Content

FREE Resource

The video tutorial covers the application of neural networks using TensorFlow. It begins with an introduction to TensorFlow and its installation, followed by data preparation using the Titanic dataset. The tutorial then explains how to define a neural network architecture using Keras, train the model, and evaluate its performance. Finally, it explores experimenting with hyperparameters and different optimizers to improve model accuracy.

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10 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which library is recommended for installing TensorFlow due to its ability to handle dependencies?

Homebrew

Conda

Yum

PIP

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What type of classification problem is the Titanic dataset used for in this tutorial?

Multiclass classification

Binary classification

Regression

Clustering

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which function is used to split the dataset into training and testing sets?

divide_dataset

partition_data

split_data

train_test_split

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the activation function used in the first hidden layer of the neural network?

Softmax

ReLU

Tanh

Sigmoid

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of dropout in a neural network layer?

To reduce the number of layers

To speed up training

To prevent overfitting

To increase the number of neurons

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which optimizer is initially used in the tutorial for training the model?

SGD

Adagrad

RMSprop

Adam

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary metric used to evaluate the model's performance?

Precision

Accuracy

Recall

F1 Score

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