Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models.
About This Book
- Learn Theano basics and evaluate your mathematical expressions faster and in an efficient manner
- Learn the design patterns of deep neural architectures to build efficient and powerful networks on your datasets
- Apply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models.
Who This Book Is For
This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets.
Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus.
What You Will Learn
- Get familiar with Theano and deep learning
- Provide examples in supervised, unsupervised, generative, or reinforcement learning.
- Discover the main principles for designing efficient deep learning nets: convolutions, residual connections, and recurrent connections.
- Use Theano on real-world computer vision datasets, such as for digit classification and image classification.
- Extend the use of Theano to natural language processing tasks, for chatbots or machine translation
- Cover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environment
- Generate synthetic data that looks real with generative modeling
- Become familiar with Lasagne and Keras, two frameworks built on top of Theano
This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes ...