Deep Learning Cookbook
Practical Recipes to Get Started Quickly
Nonfiction, Computers, Advanced Computing, Computer Science, Database Management, Data Processing, Application Software
| Author: | Douwe Osinga | ISBN: | 9781491995792 |
| Publisher: | O'Reilly Media | Publication: | June 5, 2018 |
| Imprint: | O'Reilly Media | Language: | English |
| Author: | Douwe Osinga |
| ISBN: | 9781491995792 |
| Publisher: | O'Reilly Media |
| Publication: | June 5, 2018 |
| Imprint: | O'Reilly Media |
| Language: | English |
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.
Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks.
You’ll learn how to:
- Create applications that will serve real users
- Use word embeddings to calculate text similarity
- Build a movie recommender system based on Wikipedia links
- Learn how AIs see the world by visualizing their internal state
- Build a model to suggest emojis for pieces of text
- Reuse pretrained networks to build an inverse image search service
- Compare how GANs, autoencoders and LSTMs generate icons
- Detect music styles and index song collections
Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music.
Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks.
You’ll learn how to:
- Create applications that will serve real users
- Use word embeddings to calculate text similarity
- Build a movie recommender system based on Wikipedia links
- Learn how AIs see the world by visualizing their internal state
- Build a model to suggest emojis for pieces of text
- Reuse pretrained networks to build an inverse image search service
- Compare how GANs, autoencoders and LSTMs generate icons
- Detect music styles and index song collections
