Deep Learning Pipeline: Building a Deep Learning Model with TensorFlow 1st Edition Hisham 1484253493 9781484253496

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Product details:

• ISBN-10: 1484253493 
• ISBN-13: 9781484253496
• Author: Hisham El-Amir

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts.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 Who This Book Is For Developers, analysts, and data scientists looking to add to or enhance their existing skills by accessing some of the most powerful recent trends in data science. Prior experience in Python or other TensorFlow related languages and mathematics would be helpful.

Table of contents:

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

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