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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

NLP with TensorFlow

Text data is inherently sequential, defined by the order in which words occur. Words follow one another, building upon previous ideas and shaping those to come. Understanding the sequence of words and the context in which they are applied is straightforward for humans. However, this poses a significant challenge to feed-forward networks such as convolutional neural networks (CNNs) and traditional deep neural networks (DNNs). These models treat text data as independent inputs; hence, they miss the interconnected nature and flow of language. For example, let’s take the sentence “The cat, which is a mammal, likes to chase mice." Humans immediately recognize the relationship between the cat and mice, as we process the entire sentence as a whole and not individual units.

A recurrent neural network (RNN) is a type of neural network designed to handle sequential data such as text and time-series data. When working with text data, RNNs’ memory...

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TensorFlow Developer Certificate Guide
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