Book Image

Deep Learning with Theano

By : Christopher Bourez
Book Image

Deep Learning with Theano

By: Christopher Bourez

Overview of this book

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU. The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy. The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.
Table of Contents (22 chapters)
Deep Learning with Theano
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

The future of artificial intelligence


Chapter 2, Classifying Handwritten Digits with a Feedforward Network presented diverse optimization techniques (Adam, RMSProp, and so on) and mentioned second order optimization techniques. A generalization would be to also learn the update rule:

Here, is the parameter of the optimizer to learn from different problem instances, a sort of generalization or transfer learning of the optimizer from problems to learn better on new problems. The objective to minimize under this learning to learn or meta-learning framework has to optimize the time to learn correctly and, consequently, be defined on multiple timesteps:

Where:

A recurrent neural network can be used as the optimizer model . Such a generalization technique that solves a multi-objective optimization problem improves the learning rate of the neural networks in general.

Researchers have been looking one step further, searching for general artificial intelligence, which aims for a human-level skill...