Packt+ | Advance your knowledge in tech

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

2. Classifying Handwritten Digits with a Feedforward Network FREE CHAPTER

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:

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:

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 $19.99/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.

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.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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