Key FeaturesLearn Theano basics and evaluate your mathematical expressions faster and in an efficient mannerLearn the design patterns of deep neural architectures to build efficient and powerful networks on your datasetsApply your knowledge to concrete fields such as image classification, object detection, chatbots, machine translation, reinforcement agents, or generative models.Book DescriptionThis 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.What you will learnGet familiar with Theano and deep learningProvide 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 translationCover artificial intelligence-driven strategies to enable a robot to solve games or learn from an environmentGenerate synthetic data that looks real with generative modelingBecome familiar with Lasagne and Keras, two frameworks built on top of TheanoAbout the AuthorChristopher Bourez graduated from Ecole Polytechnique and Ecole Normale Superieure de Cachan in Paris in 2005 with a Master of Science in Math, Machine Learning and Computer Vision (MVA).For 7 years, he led a company in computer vision that launched Pixee, a visual recognition application for iPhone in 2007, with the major movie theater brand, the city of Paris and the major ticket broker: with a snap of a picture, the user could get information about events, products, and access to purchase.While working on missions in computer vision with Caffe, TensorFlow or Torch, he helped other developers succeed by writing on a blog on computer science. One of his blog posts, a tutorial on the Caffe deep learning technology, has become the most successful tutorial on the web after the official Caffe website.On the initiative of Packt Publishing, the same recipes that made the success of his Caffe tutorial have been ported to write this book on Theano technology. In the meantime, a wide range of problems for Deep Learning are studied to gain more practice with Theano and its application.Table of ContentsTheano basicsClassify handwritten digits with a feedforward networkEncode word into vectorGenerate Text with a Recurrent Neural NetAnalyze Sentiment with a Bidirectional LSTMLocate with Spatial Transformer NetworksClassify Images with Residual NetworksTranslate and explain with encoding-decoding networksSelect relevant inputs or memories with the mechanism of attentionPredict Times Sequences with Advanced RNNLearning from the Environment with ReinforcementLearn Features with Unsupervised Generative NetworksExtending Theano - Whats next?