Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4 (2)
Book Image

TensorFlow Developer Certificate Guide

4 (2)
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)
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

The anatomy of RNNs

In the previous section, we talked about RNNs’ ability to handle sequential data; let’s drill down into how an RNN does this. The key differentiator between RNNs and feed-forward networks is their internal memory, as shown in Figure 11.1, which enables RNNs to process input sequences while retaining information from previous steps. This attribute empowers RNNs to suitably exploit the temporal dependencies in sequences such as text data.

Figure 11.1 – The anatomy of an RNN

Figure 11.1 – The anatomy of an RNN

Figure 11.2 shows a clearer picture of an RNN and its inner workings. Here, we can see a series of interconnected units through which data flows in a sequential fashion, one element at a time. As each unit processes the input data, it sends the output to the next unit in a similar fashion to how feed-forward networks work. The key difference lies in the feedback loop, which equips RNNs with the memory of previous inputs, empowering them with the...