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State-of-the-art deep learning models in Tensorflow : modern machine learning in the Google colab ecosystem /
Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[United States] :
Apress,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Paper, David. | |
| 245 | 1 | 0 | |a State-of-the-art deep learning models in Tensorflow : |b modern machine learning in the Google colab ecosystem / |c David Paper. |
| 260 | |a [United States] : |b Apress, |c 2021. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 505 | 0 | |a 1. Build TensorFlow Input Pipelines -- 2. Increase the Diversity of your Dataset with Data Augmentation -- 3. TensorFlow Datasets -- 4. Deep Learning with TensorFlow Datasets -- 5. Introduction to Tensor Processing Units -- 6. Simple Transfer Learning with TensorFlow Hub -- 7. Advanced Transfer Learning -- 8. Stacked Autoencoders -- 9. Convolutional and Variational Autoencoders -- 10. Generative Adversarial Networks -- 11. Progressive Growing Generative Adversarial Networks -- 12. Fast Style Transfer -- 13. Object Detection -- 14. An Introduction to Reinforcement Learning. | |
| 500 | |a Includes index. | ||
| 520 | |a Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will Learn Take advantage of the built-in support of the Google Colab ecosystem Work with TensorFlow data sets Create input pipelines to feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code Leverage pre-trained deep learning models to solve complex machine learning tasks Create a simple environment to teach an intelligent agent to make automated decisions. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed September 14, 2021). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a TensorFlow. |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Reinforcement learning. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Reinforcement learning |2 fast | |
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