Learn TensorFlow 2.0

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. You will:

• Review the new features of TensorFlow 2.0
• Use TensorFlow 2.0 to build machine learning and deep learning models
• Perform sequence predictions using TensorFlow 2.0
• Deploy TensorFlow 2.0 models with practical examples

Autorentext
Pramod Singh is currently playing a role of Machine Learning Expert at Walmart Labs. He has extensive hands-on experience in machine learning, deep learning, AI, data engineering, designing algorithms and application development. He has spent more than 10 years working on multiple data projects at different organizations. He's the author of three books -Machine Learning with PySpark , Learn PySpark and Learn TensorFlow 2.0. He is also a regular speaker at major conferences such as O'Reilly's Strata and AI conferences. Pramod holds a BTech in electrical engineering from B.A.T.U, and an MBA from Symbiosis University. He has also done Data Science certification from IIMCalcutta. He lives in Bangalore with his wife and three-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football.

Avinash Manure is a Senior Data Scientist at Publicis Sapient with over 8 years of experience in solving real-world business challenges using Data. He is proficient in deploying complex machine learning and statistical modeling algorithms/techniques for identifying patterns and extracting valuable insights for key stakeholders and organizational leadership.

Avinash holds a bachelor's degree in Electronics Engineering from Mumbai University and has done his Master's in Business Administration (Marketing) from University of Pune. He is currently settled in Bangalore with his wife. He enjoys travelling to new places and reading motivational books.

Inhalt
Learn TensorFlow 2.0
Chapter 1: TensorFlow 2.0 An Introduction Chapter Goal: Introducing TensorFlow, major features, version 2.0 release.
Chapter 2: Supervised Learning with TensorFlow 2.0Chapter Goal: Implementation of linear, logistic, SVM (Support Vector Machines) and random forest using TensorFlow.
Chapter 3: Neural Networks and Deep Learning with TensorFlow 2.0Chapter Goal: Introduction to neural networks, deep learning and implementation using TensorFlow This chapter offers a detailed view of building Deep Learning models for various applications such as Forecasting using TensorFlow 2.0. The chapter also introduces optimization approaches and the techniques for hyper parameter tuning.
Chapter 4: Images with TensorFlow 2.0Chapter Goal: TensorFlow 2.0 for images. This chapter focuses on building deep learning based models for image classification using TensorFlow 2.0. It covers advanced techniques such as GANs and transfer learning to image processing and classifications
Chapter 5: Sequence to Sequence Modeling with TensorFlow 2.0
Chapter Goal: To understand sequence modeling using TensorFlow 2.0. This chapter covers the process of using different neural networks for NLP based tasks in TensorFlow 2.0. This includes sequence to sequence prediction, text translation using deep learning in TensorFlow 2.0
Chapter 6: TensorFlow 2.0 Models in Productionization
Chapter Goal: Implementation of distributed training using TensorFlow. This chapter covers the process of scaling up the machine learning model training by implementing distributed training of TensorFlow models and deploying those models into production using TensorFlow serving layer