Deep Learning Pipeline
Apress (Verlag)
978-1-4842-5348-9 (ISBN)
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.
Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meetups, conferences, and other events. Mahmoud Hamdy is a machine learning engineer who works in Egypt and lives in Egypt, His primary area of study is the overlap between knowledge, logic, language, and learning. He works helping train machine learning, and deep learning models to distil large amounts of unstructured, semi-structured, and structured data into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. He applies strong theoretical and practical skills in several areas of machine learning to finding novel and effective solutions for interesting and challenging problems in such interconnections
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.
| Erscheinungsdatum | 30.12.2019 |
|---|---|
| Zusatzinfo | 214 Illustrations, black and white; XXV, 551 p. 214 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Schlagworte | Deep learning • deep learning pipeline • machine learning • Python • R Programming • tensorflow |
| ISBN-10 | 1-4842-5348-5 / 1484253485 |
| ISBN-13 | 978-1-4842-5348-9 / 9781484253489 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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