Deep Learning with TensorFlow and Keras: and deploy supervised unsupervised deep and reinforcement learning models ebook

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Build cutting edge machine and deep learning systems for the lab, production, and mobile devices. . 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 Tensor Flow Hub, and joint and contrastive learning Learn cutting-edge machine and deep learning techniques Book Description Deep Learning with Tensor Flow and Keras teaches you neural networks and deep learning techniques using Tensor Flow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. Tensor Flow 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 Tensor Flow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (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 Tensor Flow with Auto ML. What you will learn Learn how to use the popular GNNs with Tensor Flow 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 Tensor Flow Probability Train your models on the cloud and put TF to work in real environments Build machine learning and deep learning systems with Tensor Flow 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 Tensor Flow. This book gives you the theory and practice required to use Keras, Tensor Flow, and Auto ML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.Deep Learning with Tensor Flow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition 3rd Edition is written by Amita Kapoor; Antonio Gulli; Sujit Pal; Francois Chollet and published by Packt Publishing. ISBNs for Deep Learning with Tensor Flow and Keras are 9781803245713, 1803245719 and the print ISBNs are 9781803232911, 1803232919.

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Build cutting edge machine and deep learning systems for the lab, production, and mobile devices. . 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 Tensor Flow Hub, and joint and contrastive learning Learn cutting-edge machine and deep learning techniques Book Description Deep Learning with Tensor Flow and Keras teaches you neural networks and deep learning techniques using Tensor Flow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. Tensor Flow 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 Tensor Flow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (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 Tensor Flow with Auto ML. What you will learn Learn how to use the popular GNNs with Tensor Flow 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 Tensor Flow Probability Train your models on the cloud and put TF to work in real environments Build machine learning and deep learning systems with Tensor Flow 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 Tensor Flow. This book gives you the theory and practice required to use Keras, Tensor Flow, and Auto ML to build machine learning systems. Some machine learning knowledge would be useful. We don’t assume TF knowledge.Deep Learning with Tensor Flow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition 3rd Edition is written by Amita Kapoor; Antonio Gulli; Sujit Pal; Francois Chollet and published by Packt Publishing. ISBNs for Deep Learning with Tensor Flow and Keras are 9781803245713, 1803245719 and the print ISBNs are 9781803232911, 1803232919.