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TensorFlow Developer Certificate Guide

You're reading from   TensorFlow Developer Certificate Guide Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803240138
Length 344 pages
Edition 1st Edition
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Author (1):
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Oluwole Fagbohun Oluwole Fagbohun
Author Profile Icon Oluwole Fagbohun
Oluwole Fagbohun
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Toc

Table of Contents (20) Chapters Close

Preface 1. Part 1 – Introduction to TensorFlow
2. Chapter 1: Introduction to Machine Learning FREE CHAPTER 3. Chapter 2: Introduction to TensorFlow 4. Chapter 3: Linear Regression with TensorFlow 5. Chapter 4: Classification with TensorFlow 6. Part 2 – Image Classification with TensorFlow
7. Chapter 5: Image Classification with Neural Networks 8. Chapter 6: Improving the Model 9. Chapter 7: Image Classification with Convolutional Neural Networks 10. Chapter 8: Handling Overfitting 11. Chapter 9: Transfer Learning 12. Part 3 – Natural Language Processing with TensorFlow
13. Chapter 10: Introduction to Natural Language Processing 14. Chapter 11: NLP with TensorFlow 15. Part 4 – Time Series with TensorFlow
16. Chapter 12: Introduction to Time Series, Sequences, and Predictions 17. Chapter 13: Time Series, Sequences, and Prediction with TensorFlow 18. Index 19. Other Books You May Enjoy

Introduction to Natural Language Processing

Today, we are faced with a staggering amount of text data coming at us from all directions, from social media platforms to email communications, and from text messages to online reviews. This exponential growth in text data has led to a rapid growth in the development of text-based applications powered by advanced deep learning techniques, used to unlock insights from text data. We find ourselves in the dawn of a transformative era, powered by tech giants such as Google and Microsoft and revolutionary start-ups such as OpenAI and Anthropic. These visionaries are leading the charge in building powerful solutions capable of solving a myriad of text-based challenges, such as summarizing large volumes of documents, extracting sentiments from online platforms, and generating text for blog posts – the list of uses is endless.

Real-world text data can be messy; it could be riddled with unwanted information such as punctuation marks, special...

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