Title
Deep Learning with TensorFlow 2 and Keras Second Edition: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 an,Used
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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book DescriptionDeep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks) to create new data that fits with existing patterns Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who this book is forThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected. Table of Contents Neural Network Foundations with TensorFlow 2.0 TensorFlow 1.x and 2.x Regression Convolutional Neural Networks Advanced Convolutional Neural Networks Generative Adversarial Networks Word Embeddings Recurrent Neural Networks Autoencoders Unsupervised Learning Reinforcement Learning TensorFlow and Cloud TensorFlow for Mobile and IoT and TensorFlow.js An introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit
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We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Deep Learning with TensorFlow 2 and Keras'? A: The book primarily focuses on teaching readers how to build machine learning and deep learning systems using TensorFlow 2 and Keras, covering key techniques and applications such as regression, convolutional neural networks, generative adversarial networks, and natural language processing.
- Q: Who is the intended audience for this book? A: This book is designed for Python developers and data scientists, whether they are new to machine learning or have prior experience, looking to learn how to implement machine learning and deep learning through TensorFlow and Keras.
- Q: What are the key features of TensorFlow 2 mentioned in the book? A: The book highlights TensorFlow 2's full integration with Keras, which simplifies the building of advanced machine learning models, along with its capabilities for production, mobile deployment, and ease of use for training models in various environments.
- Q: How does the book approach teaching deep learning concepts? A: The book explains deep learning fundamentals through clear explanations and extensive code samples, enabling readers to understand complex concepts while applying them in practical scenarios.
- Q: Are there any practical applications included in the book? A: Yes, the book includes two working example applications that demonstrate how to implement machine learning and deep learning techniques using TensorFlow 2 and Keras.
- Q: What topics are covered in the table of contents? A: The table of contents includes topics such as neural network foundations, regression, convolutional neural networks, generative adversarial networks, recurrent neural networks, and an introduction to AutoML.
- Q: What is the significance of Keras in deep learning as discussed in the book? A: Keras is significant because it provides a user-friendly API for accessing TensorFlow, making it easier for developers to build and experiment with deep learning models, thus enhancing productivity and learning.
- Q: What are some advanced topics covered in the book? A: Advanced topics include generative adversarial networks, reinforcement learning, and deploying TensorFlow models for mobile and IoT applications, providing a comprehensive look at modern deep learning techniques.
- Q: When was the second edition of this book published? A: The second edition of 'Deep Learning with TensorFlow 2 and Keras' was published on December 27, 2019.
- Q: What is the binding type and page count of the book? A: The book is available in paperback binding and contains a total of 646 pages.