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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

Understanding sequential data processing – from traditional neural networks to RNNs and LSTMs

In traditional neural networks, as we discussed earlier in this book, we see an arrangement of densely interconnected neurons, devoid of any form of memory. When we feed a sequence of data to these networks, it’s an all-or-nothing transaction – the entire sequence is processed at once and converted into a singular vector representation. This approach is quite different from how humans process and comprehend text data. When we read, we naturally analyze text word by word, understanding that important words – those that have the power to shift the entire message of a sentence – can be positioned anywhere within it. For example, let's consider the sentence “I loved the movie, despite some critics.” Here, the word “despite” is pivotal, altering the direction of the sentiment expressed in the sentence.

RNNs don’t just...

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TensorFlow Developer Certificate Guide
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