Practical deep learning for cloud, mobile, and edge real-world AI and computer-vision projects using Python, Keras, and TensorFlow
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Beijing :
O'Reilly
2019.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630798406719 |
Tabla de Contenidos:
- 1. Exploring the Landscape of Artificial Intelligence
- 2. What’s in the Picture: Image Classification with Keras
- 3. Cats Versus Dogs: Transfer Learning in 30 Lines with Keras
- 4. Building a Reverse Image Search Engine: Understanding Embeddings
- 5. From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy
- 6. Maximizing Speed and Performance of TensorFlow: A Handy Checklist
- 7. Practical Tools, Tips, and Tricks
- 8. Cloud APIs for Computer Vision: Up and Running in 15 Minutes
- 9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
- 10. AI in the Browser with TensorFlow.js and ml5.js
- 11. Real-Time Object Classification on iOS with Core ML
- 12. Not Hotdog on iOS with Core ML and Create ML
- 13. Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit
- 14. Building the Purrfect Cat Locator App with TensorFlow Object Detection API
- 15. Becoming a Maker: Exploring Embedded AI at the Edge
- 16. Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras
- 17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer
- A Crash Course in Convolutional Neural Networks
- Index.
