Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow


Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow
By 作者: Hisham El-Amir - Mahmoud Hamdy
ISBN-10 书号: 1484253485
ISBN-13 书号: 9781484253489
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-12-21
pages 页数: (551 )

Book Description to Finelybook sorting

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts.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!
What You’ll Learn

Develop a deep learning project using data
Study and apply various models to your data
Debug and troubleshoot the proper model suited for your data

Part l. Introduction
1. AGentle Introduction
2. Setting Up Your Environment
3.A Tour Through the Deep Learning Pipeline
4. Build Your First Toy TensorFlow app
Part ll. Data
5. Defining Data
6. Data Wrangling and Preprocessing
7. Data Resampling
8. Feature Selection and Feature Engineering
Part ll. TensorFlow
9. Deep Learning Fundamentals
10. Improving Deep Neural Networks
11. Convolutional Neural Network
12. Sequential Models
Part V. Applying What You' ve Learned
13. Selected Topics in Computer Vision
14. Selected Topics in Natural Language Processing
15. Applications

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