Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

Deep Learning with Theano

You're reading from Deep Learning with Theano Perform large-scale numerical and scientific computations efficiently

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786465825

Length 300 pages

Edition 1st Edition

Tools

Theano

Concepts

Deep Learning

Author (1):

Christopher Bourez

View More author details

Table of Contents (15) Chapters

Preface

1. Theano Basics FREE CHAPTER

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

Index

Continuous Bag of Words model

The design of the neural network to predict a word given its surrounding context is shown in the following figure:

Continuous Bag of Words model

The input layer receives the context while the output layer predicts the target word. The model we'll use for the CBOW model has three layers: input layer, hidden layer (also called the projection layer or embedding layer), and output layer. In our setting, the vocabulary size is V and the hidden layer size is N. Adjacent units are fully connected.

The input and the output can be represented either by an index (an integer, 0-dimensional) or a one-hot-encoding vector (1-dimensional). Multiplying with the one-hot-encoding vector v consists simply of taking the j-th row of the embedding matrix:

Continuous Bag of Words model

Since the index representation is more efficient than the one-hot encoding representation in terms of memory usage, and Theano supports indexing symbolic variables, it is preferable to adopt the index representation as much as possible.

Therefore, input (context...

The rest of the chapter is locked

Register for a free Packt account to unlock a world of extra content!

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at £16.99/month. Cancel anytime

Authors (1)

Bourez

Bourez

Christopher Bourez graduated from Ecole Polytechnique and Ecole Normale Suprieure 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.

See other products by Bourez

Other recommended products

Related to this chapter

Python High Performance

Python High Performance

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries makes Python a wildly popular language.

Recurrent Neural Networks with Python Quick Start Guide

Recurrent Neural Networks with Python Quick Start Guide

Developers struggle to find an easy to follow learning resource for implementing Recurrent Neural Network(RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve the results. This book will teach you the fundamentals of RNNs with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.

Deep Learning Essentials

Deep Learning Essentials

Deep Learning is one of the trending topics in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps when it comes to training efficient deep learning models, and apply them in various practical scenarios. You will model, train and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and see their applications in real-world domains such as computer vision, natural language processing, and speech recognition. This book also covers solutions to tackle different problems you might come across while training your models and ensure their high performance. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.

Jan 2018 9h 28m

Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

TensorFlow is the leading framework for deep learning algorithms critical to artificial intelligence, and natural language processing (NLP) makes much of the data used by deep learning applications accessible to them. This book brings the two together and teaches deep learning developers how to work with today's vast amount of unstructured data.

May 2018 15h 44m

Practical Convolutional Neural Networks

Practical Convolutional Neural Networks

This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN's best practices to implement smart ConvNet models and apply them to solve complex deep learning problems.

Feb 2018 7h 16m

Keras Deep Learning Cookbook

Keras Deep Learning Cookbook

This book gives you a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning. It presents a unique problem-solution approach to tackle various problems in training different types of neural networks while taking care of the speed and accuracy of these models

Oct 2018 8h 24m

PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.

Apr 2019 8h 20m

Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.

May 2019 12h 24m

Mastering Computer Vision with TensorFlow 2.x

Mastering Computer Vision with TensorFlow 2.x

You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. You will learn how to use TensorFlow 2.x to build your own neural network model and apply it to various computer vision tasks such as image acquiring, processing, and analyzing.

May 2020 14h 20m

Deep Learning with Keras

Deep Learning with Keras

Keras is a high-level neural network library written in Python that runs on top of either Theano or TensorFlow. With this book, you'll learn the basics of Keras in a highly practical way and understand how this minimal, highly modular framework runs on both CPU and GPU, allowing you to put your ideas into action in the shortest possible time.

Apr 2017 10h 36m

Advanced Deep Learning with Python

Advanced Deep Learning with Python

This book is an expert-level guide to master the neural network variants using the Python ecosystem. You will gain the skills to build smarter, faster, and efficient deep learning systems with practical examples. By the end of this book, you will be up to date with the latest advances and current researches in the deep learning domain.

Dec 2019 15h 36m

Hands-On Generative Adversarial Networks with Keras

Hands-On Generative Adversarial Networks with Keras

This book will explore deep learning and generative models, and their applications in artificial intelligence. You will learn to evaluate and improve your GAN models by eliminating challenges that are encountered in real-world applications. You will implement GAN architectures in various domains such as computer vision, NLP, and audio processing

Personalised recommendations for you

Based on your interests and search pattern

Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch

This book provides a hands-on approach to solving over 30 prominent real-world computer vision problems using PyTorch 2.x on actual datasets. Here you'll learn to build a neural network from scratch and optimize hyperparameters, perform image classification, multi-object detection, segmentation, and more. You'll also explore facial expression manipulation and combining CV with NLP and RL techniques, build generative AI applications, and take your model to production on AWS. By the end of this book, you'll master modern NN architectures and confidently solve real-world CV problems.

Jun 2024 24h 52m

Data Governance Handbook

Data Governance Handbook

This book provides a highly focused view of real business outcomes powered by data governance, that resonate with non-data executives such as CFOs and CEOs. You'll also find useful insights into how to implement data governance initiatives.

May 2024 13h 8m

Data Engineering with Databricks Cookbook

Data Engineering with Databricks Cookbook

This book shows you how to use Apache Spark, Delta Lake, and Databricks to build data pipelines, manage and transform data, optimize performance, and more. Additionally, you'll implement DataOps and DevOps practices, and orchestrate data workflows.

May 2024 14h 36m

Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide

Unlock the power of Azure data engineering with this certification guide, elevating your skills in data processing, storage, and security with the help of practical insights, hands-on exercises, and the latest advancements.

May 2024 18h 16m

Microsoft Power BI Cookbook

Microsoft Power BI Cookbook

Microsoft Power BI is the most sought-after platform for BI professionals' visualization needs. Explore the latest Power BI features, future AI enhancements, and integration with other Power Platform tools via new recipes in this updated edition.

Jul 2024 19h 56m

Python Data Cleaning Cookbook

Python Data Cleaning Cookbook

The book shows you how to clean, wrangle, and view data from multiple perspectives, including dataset and column attributes. You will cover common and not-so-common challenges that are faced while cleaning messy data for complex situations and learn to manipulate data to get it down to a form that can be useful for making the right decisions.

May 2024 16h 12m

Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

This AI-900 study guide will help you prepare and practice for the certification exam. You'll delve into AI workloads, ML principles, computer vision, NLP, knowledge mining, and generative AI using Azure cloud services.

May 2024 9h 36m

Using Stable Diffusion with Python

Using Stable Diffusion with Python

This book shows you how to use Python to control Stable Diffusion and generate high-quality images. In addition to covering the basic usage of the diffusers package, the book provides solutions for extending the package for more advanced purposes.

Jun 2024 11h 44m

Getting Started with DuckDB

Getting Started with DuckDB

This hands-on book teaches you to analyze large datasets with blazing speed and ease. You will learn how to use DuckDB to quickly load, query, transform, analyze, and visualize data effectively through a series of practical examples.

Jun 2024 12h 44m

Databricks Certified Associate Developer for Apache Spark Using Python

Databricks Certified Associate Developer for Apache Spark Using Python

This guide gets you ready for certification with expert-backed content, key exam concepts, and topic reviews. Additionally, you'll be able to make the most of Apache Spark 3.0 to modernize workloads and more using specific tools and techniques.