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

Improving the performance of the model

Earlier, we discussed some factors that we should consider as we designed our baseline architecture for sentiment analysis in this chapter. Also, in Chapter 8, Handling Overfitting, we explored some foundational concepts to mitigate against overfitting. There, we looked at ideas such as early stopping and dropout regularization. To curb overfitting, let’s begin by tuning some of our model’s hyperparameters. To do this, let’s construct a function called sentiment_model. This function takes in three parameters – vocab_size, embedding_dim, and the size of the training set.

Increasing the size of the vocabulary

One hyperparameter we may consider changing is the size of the vocabulary. Increasing the vocabulary size empowers the model to learn more unique words from our dataset. Let’s see how this will impact the performance of our base model. Here, we adjust vocab_size from 10000 to 20000, while keeping the...