Deep Learning with Theano: Perform large-scale numerical and scientific computations efficiently - Tapa blanda

Reseña del editor

Develop deep neural networks in Theano with practical code examples for image classification, machine translation, reinforcement agents, or generative models.

Key Features

• 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.

Book Description

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.

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

Table of Contents

1. Theano Basics0
2. Classifying Handwritten Digits with a Feedforward Network
3. Encoding Word into Vector
4. Generating Text with a Recurrent Neural Net
5. Analyzing Sentiment with a Bidirectional LSTM
6. Locating with Spatial Transformer Networks
7. Classifying Images with Residual Networks
8. Translating and Explaining with Encoding - decoding Networks
9. Selecting Relevant Inputs or Memories with the Mechanism of Attention
10. Predicting Times Sequences with Advanced RNN
11. Learning from the Environment with Reinforcement
12. Learning Features with Unsupervised Generative Networks
13. Extending Deep Learning with Theano

Biografía del autor

Christopher 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.