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

Image Classification with Convolutional Neural Networks

Convolutional neural networks (CNNs) are the go-to algorithms when it comes to image classification. In the 1960s, neuroscientists Hubel and Wiesel conducted a study on the visual cortex in cats and monkeys. Their work unraveled how we visually process information in a hierarchical structure, showing how visual systems are organized into a series of layers where each layer is responsible for a different aspect of visual processing. This earned them a Nobel Prize, but more importantly, it served as the basis upon which CNNs are built. CNNs, by virtue of their nature, are well designed to work with data with spatial structures such as images.

However, in the early days, CNNs did not have the limelight due to a number of factors, such as insufficient training data, underdeveloped network architecture, insufficient computational resources, and the absence of modern techniques such as data augmentation and dropout. In the 2012 ImageNet...