Deep Learning Pipeline : Building a Deep Learning Model with TensorFlow / by Hisham El-Amir, Mahmoud Hamdy.

Format:

Book

Author/Creator:

El-Amir, Hisham., Author.

Hamdy, Mahmoud., Author.

Language:

English

Subjects (All):

Artificial intelligence.

Artificial Intelligence.

TensorFlow.

Local Subjects:

Artificial Intelligence.

Physical Description:

1 online resource (563 pages)

Edition:

1st ed. 2020.

Other Title:

Building a deep learning model with TensorFlow

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2020.

System Details:

text file

Summary:

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you!

Contents:

Deep Learning Pipeline Part One: Introduction

Chapter 1: A Gentle Introduction

Chapter 2: Setting up Your Environment

Chapter 3: A Nice Tour Through Deep Learning Pipeline

Part Two: Data

Chapter 4: Build your first Toy TensorFlow App

Chapter 5: Defining Data

Chapter 6: Data Wrangling and Preprocessing

Chapter 7: Data Resampling

Part Three: TensorFlow

Chapter 8: Feature Selection and Feature Engineering

Chapter 9: Deep Learning Fundamentals

Chapter 10: Improving Deep Neural Network

Chapter 11: Convolutional Neural Networks

Part Four: Applications and Appendix

Chapter 12: Sequential Models

Chapter 13: Selected Topics in Computer vision

Chapter 14: Selected Topics in Natural Language Processing

Chapter 15: Applications.

Notes:

Includes index.

Includes bibliographical references.

ISBN:

9781484253496

1484253493

OCLC:

1155055365