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Deep Learning with TensorFlow and Keras - Third Edition - 3rd Edition by  Amita Kapoor & Antonio Gulli & Sujit Pal (Paperback) - 1 of 1

Deep Learning with TensorFlow and Keras - Third Edition - 3rd Edition by Amita Kapoor & Antonio Gulli & Sujit Pal (Paperback)

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About this item

Highlights

  • Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features: Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook Description: Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras.
  • Author(s): Amita Kapoor & Antonio Gulli & Sujit Pal
  • 698 Pages
  • Computers + Internet, Natural Language Processing

Description



About the Book



Deep Learning with TensorFlow and Keras, Third Edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloud in production, TF mobile, and using TensorFlow with AutoML.



Book Synopsis



Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.


Purchase of the print or Kindle book includes a free eBook in PDF format.


Key Features:

  • Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
  • Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning
  • Learn cutting-edge machine and deep learning techniques


Book Description:

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using 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 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.


This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, GANs, recurrent neural networks (RNNs), natural language processing (NLP), and Graph Neural Networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.


What You Will Learn:

  • Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks
  • Discover the world of transformers, from pretraining to fine-tuning to evaluating them
  • Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
  • Combine probabilistic and deep learning models using TensorFlow Probability
  • Train your models on the cloud and put TF to work in real environments
  • Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API


Who this book is for:

This hands-on machine learning 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, and AutoML to build machine learning systems.


Some machine learning knowledge would be useful. We don't assume TF knowledge.

Dimensions (Overall): 9.25 Inches (H) x 7.5 Inches (W) x 1.4 Inches (D)
Weight: 2.6 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 698
Genre: Computers + Internet
Sub-Genre: Natural Language Processing
Publisher: Packt Publishing
Format: Paperback
Author: Amita Kapoor & Antonio Gulli & Sujit Pal
Language: English
Street Date: October 6, 2022
TCIN: 88107717
UPC: 9781803232911
Item Number (DPCI): 247-43-2089
Origin: Made in the USA or Imported
If the item details aren’t accurate or complete, we want to know about it.

Shipping details

Estimated ship dimensions: 1.4 inches length x 7.5 inches width x 9.25 inches height
Estimated ship weight: 2.6 pounds
We regret that this item cannot be shipped to PO Boxes.
This item cannot be shipped to the following locations: American Samoa (see also separate entry under AS), Guam (see also separate entry under GU), Northern Mariana Islands, Puerto Rico (see also separate entry under PR), United States Minor Outlying Islands, Virgin Islands, U.S., APO/FPO, Alaska, Hawaii

Return details

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This item must be returned within 90 days of the date it was purchased in store, delivered to the guest, delivered by a Shipt shopper, or picked up by the guest.
See the return policy for complete information.

Q: What key features does this book offer for learning deep learning?

submitted by AI Shopping Assistant - 22 days ago
  • A: The book provides extensive code samples, clear explanations, and covers advanced techniques like graph neural networks and transformers.

    submitted byAI Shopping Assistant - 22 days ago
    Ai generated

Q: What advanced applications are included in the book?

submitted by AI Shopping Assistant - 22 days ago
  • A: It explores applications like reinforcement learning, natural language processing, and creating neural networks using TensorFlow.

    submitted byAI Shopping Assistant - 22 days ago
    Ai generated

Q: Is there an additional resource provided with the purchase?

submitted by AI Shopping Assistant - 22 days ago
  • A: Yes, purchasing this book includes a free eBook in PDF format for additional convenience and learning flexibility.

    submitted byAI Shopping Assistant - 22 days ago
    Ai generated

Q: What programming language is primarily used in this book?

submitted by AI Shopping Assistant - 22 days ago
  • A: The book focuses on Python, aimed at developers and data scientists familiar with machine learning.

    submitted byAI Shopping Assistant - 22 days ago
    Ai generated

Q: Does the book cover both supervised and unsupervised learning?

submitted by AI Shopping Assistant - 22 days ago
  • A: Yes, it offers a comprehensive overview of both supervised and unsupervised machine learning models, including practical examples.

    submitted byAI Shopping Assistant - 22 days ago
    Ai generated

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