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Python deep learning : exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow /
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your pro...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2019.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
MARC
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| 100 | 1 | |a Vasilev, Ivan, |e author. | |
| 245 | 1 | 0 | |a Python deep learning : |b exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow / |c Ivan Vasilev [and four others]. |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, |c 2019. | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 588 | 0 | |a Online resource; title from title page (Safari, viewed February 15, 2019). | |
| 505 | 0 | |a Table of ContentsMachine Learning -- An IntroductionNeural NetworksDeep Learning FundamentalsComputer Vision With Convolutional NetworksAdvanced Computer VisionGenerating images with GANs and Variational AutoencodersRecurrent Neural Networks and Language ModelsReinforcement Learning TheoryDeep Reinforcement Learning for GamesDeep Learning in Autonomous Vehicles. | |
| 520 | 3 | |a With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. | |
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| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
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| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
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