This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.
In TensorFlow 2.0 in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. You'll develop a sentiment analyzer for movie reviews, an NLP spam classifier, and other hands-on projects.
about the technology
TensorFlow is the go-to framework for putting deep learning into production. Created by Google, this groundbreaking tool handles repetitive low-level operations and frees you up to focus on innovating your AIs. TensorFlow encompasses almost every element of a deep learning pipeline—a one-stop solution for building, monitoring, optimizing, and deploying your models.
about the book
TensorFlow 2.0 in Action teaches you to use the new features of TensorFlow 2.0 to create advanced deep learning models. You’ll learn by building hands-on projects, including an image classifier that can recognize objects, a French-to-English machine translator, and even a neural network that can write fiction.
Thushan Ganegedara draws on his experience as a practitioner and teacher of deep learning to demystify concepts otherwise trapped in dense academic papers. A top StackOverflow contributor for deep learning and NLP, Thushan’s quirky stories, real-world advice, and behind-the-scenes explanations make even complex ideas easy to understand! You’ll dive into the details of modern deep learning techniques, including both transformer and attention models, and learn how pretrained models can solve your tricky data science problems. When you’re done, you’ll be ready to implement state-of-the-art deep learning applications and know the design secrets behind their success.
Fundamentals of TensorFlow
Implement deep learning networks
Picking a high-level Keras API
Write comprehensive end-to-end data pipelines
Build models for computer vision and natural language processing
Utilize pretrained NLP models
Recent algorithms including transformers, attention models, and ElMo
about the reader
For Python programmers with basic machine learning skills.
about the author
Thushan Ganegedara is a data scientist with QBE. He holds a PhD in machine learning from the University of Sydney and he has worked with TensorFlow for almost 5 years. Thushan is also one of the most active answer providers for TensorFlow and TensorFlow2.0 tags on Stackoverflow, a DataCamp instructor, and has authored a book and video course on NLP with TensorFlow.
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