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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

Time Series, Sequences, and Prediction with TensorFlow

Welcome to the final chapter in our journey with TensorFlow. In the last chapter, we closed on a high by applying neural networks such as DNNs to forecast time series data effectively. In this chapter, we will be exploring an array of advanced ideas, such as integrating learning rate schedulers into our workflow to dynamically adapt our learning rate and accelerate the process of model training. In previous chapters, we emphasized the need for and importance of finding the optimal learning rate. When building models with learning rate schedulers, we can achieve this in a dynamic way either using inbuilt learning rate schedulers in TensorFlow or by crafting our own custom-made learning rate scheduler.

Next, we will discuss Lambda layers and how these arbitrary layers can be applied in our model architecture to enhance quick experimentation, enabling us to embed custom functions seamlessly into our model’s architecture...

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TensorFlow Developer Certificate Guide
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