Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
Par :Formats :
- Réservation en ligne avec paiement en magasin :
- Indisponible pour réserver et payer en magasin
- Nombre de pages350
- PrésentationBroché
- FormatGrand Format
- Poids0.633 kg
- Dimensions19,1 cm × 23,5 cm × 1,9 cm
- ISBN978-1-78862-941-6
- EAN9781788629416
- Date de parution31/10/2018
- ÉditeurPackt Publishing
Résumé
This book is a guide to advanced deep learning techniques and how to create your own cutting-edge Al. Using Keras, you'll find hands-on projects throughout that show you how to create effective Al with the latest techniques. Professor Atienza provides an overview of MLPs, CNNs, and RNNs, the building blocks for more advanced techniques. You'll learn how to implement deep learning with Keras and Tensorflow.
You'll also explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. Learn about generative adversarial networks (GANs), and how they can open new levels of Al performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll finish by implementing Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in Al.
Things you will learn :- Cutting-edge techniques in human-like Al performance. - Implement advanced deep learning models using Keras. - The building blocks for advanced techniques (MLPs, CNNs, and RNNs). - Deep neural networks (ResNet and DenseNet). - Autoencoders and Variational AutoEncoders (VAEs). - Generative Adversarial Networks (GANs) and creative Al techniques. - Disentangled Representation GANs, and Cross-Domain GANs.
- Deep Reinforcement Learning (DRL) methods and implementation. - Produce industry-standard applications usingOpenAl gym. - Deep Q-Learning and Policy. Gradient Methods.
You'll also explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. Learn about generative adversarial networks (GANs), and how they can open new levels of Al performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll finish by implementing Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in Al.
Things you will learn :- Cutting-edge techniques in human-like Al performance. - Implement advanced deep learning models using Keras. - The building blocks for advanced techniques (MLPs, CNNs, and RNNs). - Deep neural networks (ResNet and DenseNet). - Autoencoders and Variational AutoEncoders (VAEs). - Generative Adversarial Networks (GANs) and creative Al techniques. - Disentangled Representation GANs, and Cross-Domain GANs.
- Deep Reinforcement Learning (DRL) methods and implementation. - Produce industry-standard applications usingOpenAl gym. - Deep Q-Learning and Policy. Gradient Methods.
This book is a guide to advanced deep learning techniques and how to create your own cutting-edge Al. Using Keras, you'll find hands-on projects throughout that show you how to create effective Al with the latest techniques. Professor Atienza provides an overview of MLPs, CNNs, and RNNs, the building blocks for more advanced techniques. You'll learn how to implement deep learning with Keras and Tensorflow.
You'll also explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. Learn about generative adversarial networks (GANs), and how they can open new levels of Al performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll finish by implementing Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in Al.
Things you will learn :- Cutting-edge techniques in human-like Al performance. - Implement advanced deep learning models using Keras. - The building blocks for advanced techniques (MLPs, CNNs, and RNNs). - Deep neural networks (ResNet and DenseNet). - Autoencoders and Variational AutoEncoders (VAEs). - Generative Adversarial Networks (GANs) and creative Al techniques. - Disentangled Representation GANs, and Cross-Domain GANs.
- Deep Reinforcement Learning (DRL) methods and implementation. - Produce industry-standard applications usingOpenAl gym. - Deep Q-Learning and Policy. Gradient Methods.
You'll also explore deep neural network architectures, including ResNet and DenseNet, and how to create Autoencoders. Learn about generative adversarial networks (GANs), and how they can open new levels of Al performance. Variational AutoEncoders (VAEs) are implemented, and you'll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll finish by implementing Deep Reinforcement Learning (DRL) such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in Al.
Things you will learn :- Cutting-edge techniques in human-like Al performance. - Implement advanced deep learning models using Keras. - The building blocks for advanced techniques (MLPs, CNNs, and RNNs). - Deep neural networks (ResNet and DenseNet). - Autoencoders and Variational AutoEncoders (VAEs). - Generative Adversarial Networks (GANs) and creative Al techniques. - Disentangled Representation GANs, and Cross-Domain GANs.
- Deep Reinforcement Learning (DRL) methods and implementation. - Produce industry-standard applications usingOpenAl gym. - Deep Q-Learning and Policy. Gradient Methods.