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Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python

Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python

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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.

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About the Book

Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 and featuring TensorFlow 2, the Keras API, CNNs, GANs, RNNs, NLP, and AutoML, has now been published.


Key Features:

  • Implement various deep learning algorithms in Keras and see how deep learning can be used in games
  • See how various deep learning models and practical use-cases can be implemented using Keras
  • A practical, hands-on guide with real-world examples to give you a strong foundation in Keras


Book Description:

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided.


Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer.


Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.


What You Will Learn:

  • Optimize step-by-step functions on a large neural network using the Backpropagation algorithm
  • Fine-tune a neural network to improve the quality of results
  • Use deep learning for image and audio processing
  • Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
  • Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
  • Explore the process required to implement Autoencoders
  • Evolve a deep neural network using reinforcement learning


Who this book is for:

If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book.


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Product Details
  • ISBN-13: 9781787128422
  • Publisher: Packt Publishing
  • Publisher Imprint: Packt Publishing
  • Height: 235 mm
  • No of Pages: 318
  • Spine Width: 17 mm
  • Weight: 548 gr
  • ISBN-10: 1787128423
  • Publisher Date: 28 Apr 2017
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Implementing deep learning models and neural networks with the power of Python
  • Width: 191 mm


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