Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow

Description

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts.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. Front Matter ....Pages i-xxv Front Matter ....Pages 1-1 A Gentle Introduction (Hisham El-Amir, Mahmoud Hamdy)....Pages 3-36 Setting Up Your Environment (Hisham El-Amir, Mahmoud Hamdy)....Pages 37-56 A Tour Through the Deep Learning Pipeline (Hisham El-Amir, Mahmoud Hamdy)....Pages 57-84 Build Your First Toy TensorFlow app (Hisham El-Amir, Mahmoud Hamdy)....Pages 85-109 Front Matter ....Pages 111-111 Defining Data (Hisham El-Amir, Mahmoud Hamdy)....Pages 113-145 Data Wrangling and Preprocessing (Hisham El-Amir, Mahmoud Hamdy)....Pages 147-206 Data Resampling (Hisham El-Amir, Mahmoud Hamdy)....Pages 207-231 Feature Selection and Feature Engineering (Hisham El-Amir, Mahmoud Hamdy)....Pages 233-276 Front Matter ....Pages 277-277 Deep Learning Fundamentals (Hisham El-Amir, Mahmoud Hamdy)....Pages 279-343 Improving Deep Neural Networks (Hisham El-Amir, Mahmoud Hamdy)....Pages 345-366 Convolutional Neural Network (Hisham El-Amir, Mahmoud Hamdy)....Pages 367-413 Sequential Models (Hisham El-Amir, Mahmoud Hamdy)....Pages 415-446 Front Matter ....Pages 447-447 Selected Topics in Computer Vision (Hisham El-Amir, Mahmoud Hamdy)....Pages 449-469 Selected Topics in Natural Language Processing (Hisham El-Amir, Mahmoud Hamdy)....Pages 471-494 Applications (Hisham El-Amir, Mahmoud Hamdy)....Pages 495-535 Back Matter ....Pages 537-551