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 neural networks

In the first section of this book, we talked about models. These models that we spoke about and used for various use cases are neural networks. A neural network is a deep learning algorithm inspired by the functionality of the human brain, but by no means does it operate like the human brain. It learns useful representation of the input data using a layered approach, as shown in Figure 5.1:

Figure 5.1 – Neural network

Figure 5.1 – Neural network

Neural networks are ideal for tackling complex problems due to their ability to identify very complex patterns in data. This makes them well suited for building solutions around text and image data (unstructured data), tasks that traditional machine learning algorithms struggle with. Neural networks develop rules to map input data to the target or labels using layered representation. When we train them on labeled data, they learn the patterns and use this knowledge to map the new input data to their corresponding...