Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering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.
