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  • Book Overview & Buying TensorFlow Developer Certificate Guide
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TensorFlow Developer Certificate Guide

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4.8 (8)
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TensorFlow Developer Certificate Guide

TensorFlow Developer Certificate Guide

4.8 (8)
By: Oluwole Fagbohun

Overview of this book

The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Table of Contents (20 chapters)
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1
Part 1 – Introduction to TensorFlow
6
Part 2 – Image Classification with TensorFlow
12
Part 3 – Natural Language Processing with TensorFlow
15
Part 4 – Time Series with TensorFlow

Summary

Transfer learning has gained traction in the deep learning community, due to its improved performance, speed, and accuracy in building deep learning models. We discussed the rationale behind transfer learning and explored transfer learning as a feature extractor and a fine-tuned model. We built a couple of solutions using the top-performing pre-trained models and saw how they outperformed our baseline model when applied to the X-ray dataset.

By now, you should have gained a solid understanding of transfer learning and its applications. Equipped with this knowledge, you should be able to apply transfer learning as either a feature extractor or a fine-tuned model when building real-world deep learning solutions for a wide range of tasks.

With this, we have come to the end of this chapter and this section of the book. In the next chapter, we will discuss natural language processing (NLP), where we will build exciting NLP applications using TensorFlow.

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TensorFlow Developer Certificate Guide
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