Deep Learning with R
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- Nombre de pages335
- PrésentationBroché
- FormatGrand Format
- Poids0.666 kg
- Dimensions18,4 cm × 23,3 cm × 2,7 cm
- ISBN978-1-61729-554-6
- EAN9781617295546
- Date de parution28/03/2018
- ÉditeurManning
- AuteurJ.J. Allaire
Résumé
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface.
Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J.J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside : Deep learning from first principles ; Setting up your own deep-learning environment ; Image classification and generation ; Deep learning for text and sequences. You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J.J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside : Deep learning from first principles ; Setting up your own deep-learning environment ; Image classification and generation ; Deep learning for text and sequences. You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface.
Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J.J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside : Deep learning from first principles ; Setting up your own deep-learning environment ; Image classification and generation ; Deep learning for text and sequences. You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.
Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J.J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.
What's Inside : Deep learning from first principles ; Setting up your own deep-learning environment ; Image classification and generation ; Deep learning for text and sequences. You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.