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Cracking the Data Science Interview

You're reading from   Cracking the Data Science Interview Unlock insider tips from industry experts to master the data science field

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Product type Paperback
Published in Feb 2024
Publisher Packt
ISBN-13 9781805120506
Length 404 pages
Edition 1st Edition
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Authors (2):
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Leondra R. Gonzalez Leondra R. Gonzalez
Author Profile Icon Leondra R. Gonzalez
Leondra R. Gonzalez
Aaren Stubberfield Aaren Stubberfield
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Aaren Stubberfield
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Breaking into the Data Science Field FREE CHAPTER
2. Chapter 1: Exploring Today’s Modern Data Science Landscape 3. Chapter 2: Finding a Job in Data Science 4. Part 2: Manipulating and Managing Data
5. Chapter 3: Programming with Python 6. Chapter 4: Visualizing Data and Data Storytelling 7. Chapter 5: Querying Databases with SQL 8. Chapter 6: Scripting with Shell and Bash Commands in Linux 9. Chapter 7: Using Git for Version Control 10. Part 3: Exploring Artificial Intelligence
11. Chapter 8: Mining Data with Probability and Statistics 12. Chapter 9: Understanding Feature Engineering and Preparing Data for Modeling 13. Chapter 10: Mastering Machine Learning Concepts 14. Chapter 11: Building Networks with Deep Learning 15. Chapter 12: Implementing Machine Learning Solutions with MLOps 16. Part 4: Getting the Job
17. Chapter 13: Mastering the Interview Rounds 18. Chapter 14: Negotiating Compensation 19. Index 20. Other Books You May Enjoy

Introducing neural networks and deep learning

At its core, a neural network (also known as a neural net) is a computational model inspired by the structure and function of the human brain. It’s designed to process information and make decisions in a manner akin to how our neurons work.

An NN consists of interconnected nodes, or artificial neurons, organized into layers. These layers typically include an input layer, one or more hidden layers, and an output layer, which you can see in Figure 11.1. Each connection between neurons is associated with a weight, which determines the strength of the connection, and an activation function, which defines the output of the neuron:

Figure 11.1: Basic NN diagram

Figure 11.1: Basic NN diagram

Data passes from the input layer through the hidden layers until it reaches the final layer as an output. The preceding diagram shows two output nodes, but an NN can consist of one or even hundreds of output nodes. The number of output nodes is an...

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