Deep learning with Python a hands-on introduction
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practica...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Apress
[2017]
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| Edición: | 1st ed. 2017. |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629967206719 |
Tabla de Contenidos:
- Chapter 1: An intuitive look at the fundamentals of deep learning based on practical applications
- Chapter 2: A survey of the current state-of-the-art implementations of libraries, tools and packages for deep learning and the case for the Python ecosystem
- Chapter 3: A detailed look at Keras [1], which is a high level framework for deep learning suitable for beginners to understand and experiment with deep learning
- Chapter 4: A detailed look at Theano [2], which is a low level framework for implementing architectures and algorithms in deep learning from scratch
- Chapter 5: A detailed look at Caffe [3], which is highly optimized framework for implementing some of the most popular deep learning architectures (mainly computer vision)
- Chapter 6: A brief introduction to GPUs and why they are a game changer for Deep Learning
- Chapter 7: A brief introduction to Automatic Differentiation
- Chapter 8: A brief introduction to Backpropagation and Stochastic Gradient Descent
- Chapter 9: A survey of Deep Learning Architectures
- Chapter 10: Advice on running large scale experiments in deep learning and taking models to production. - Chapter 11: Introduction to Tensorflow. - Chapter 12: Introduction to PyTorch. -Chapter 13: Regularization Techniques. - Chapter 14: Training Deep Leaning Models.
