This is the code repository for Deep Learning with Theano, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
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.
Further, the book speaks about 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 for the reader to build new custom nets.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter 03.
The code will look like the following:
from utils import *
from model import CBOW
import math
import numpy as np
import six.moves.cPickle as pickle
Theano installation requires conda or pip, and the install process is the same under Windows, Mac OS and Linux. The code has been tested under Mac OS and Linux Ubuntu. There might be some specificities for Windows, such as modifying the paths, that the Windows developer will solve quite easily.
For chapter 01, use the code from the chapter since the code is only Python shell commands.
For chapters 07, 08, 09, code will be published soon. Use the code from the chapter in the mean time.