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Note This tutorial is adapted from an existing convolution arithmetic guide [1], with an added emphasis on Theano’s interface. Also, note that the signal processing community has a different nomenclature and a well established literature on the topic, but for this tutorial we will stick to the terms used in the machine learning community. For a signal processing point of view on the subject, see f
Summary¶ This tutorial aims to provide an example of how a Recurrent Neural Network (RNN) using the Long Short Term Memory (LSTM) architecture can be implemented using Theano. In this tutorial, this model is used to perform sentiment analysis on movie reviews from the Large Movie Review Dataset, sometimes known as the IMDB dataset. In this task, given a movie review, the model attempts to predict
Using the GPU¶ For an introductory discussion of Graphical Processing Units (GPU) and their use for intensive parallel computation purposes, see GPGPU. One of Theano’s design goals is to specify computations at an abstract level, so that the internal function compiler has a lot of flexibility about how to carry out those computations. One of the ways we take advantage of this flexibility is in car
Piano-midi.de: classical piano pieces (http://www.piano-midi.de/) Nottingham : over 1000 folk tunes (http://abc.sourceforge.net/NMD/) MuseData: electronic library of classical music scores (http://musedata.stanford.edu/) JSB Chorales: set of four-part harmonized chorales (http://www.jsbchorales.net/index.shtml) FMA: A Dataset For Music Analysis (https://github.com/mdeff/fma) Natural Images MNIST:
Warning This project does not have any current developer. We will continue to review pull requests and merge them when appropriate, but do not expect new development unless someone decides to work on it. There are other machine learning frameworks built on top of Theano that could interest you, such as: Blocks, Keras and Lasagne. Don’t expect a clean road without bumps! If you find a bug please wr
Tutorial¶ Let us start an interactive session (e.g. with python or ipython) and import Theano. Several of the symbols you will need to use are in the tensor subpackage of Theano. Let us import that subpackage under a handy name like T (the tutorials will frequently use this convention). If that succeeded you are ready for the tutorial, otherwise check your installation (see Installing Theano). Thr
Modeling and generating sequences of polyphonic music with the RNN-RBM¶ Note This tutorial demonstrates a basic implementation of the RNN-RBM as described in [BoulangerLewandowski12] (pdf). We assume the reader is familiar with recurrent neural networks using the scan op and restricted Boltzmann machines (RBM). Note The code for this section is available for download here: rnnrbm.py. You will need
© Copyright 2011-2015, LISA lab. Last updated on May 26, 2017. Created using Sphinx 1.5.Theme by vkvn
Deep Learning… moving beyond shallow machine learning since 2006! Theano – CPU/GPU symbolic expression compiler in python (from MILA lab at University of Montreal) Torch – provides a Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu) Pylearn2 – Pylearn2 is a library designed to make machine learning research
Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithmetic ops, T.grad, floatX, pool , conv2d, dimshuffle. If you intend to run the code on GPU also read GPU. To run this example on a GPU, you need a good
Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally it uses the following Theano functions and concepts: T.tanh, shared variables, basic arithmetic ops, T.grad, Random numbers, floatX. If you intend to run the code on GPU also read GPU. The Denoising Autoencoder (dA) is an extension of a classical
Once your setup is complete and if you installed the GPU libraries, head to Testing Theano with GPU to find how to verify everything is working properly. To update your current installation see Updating Theano.
Books on Deep Learning Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation. Survey Papers on Deep Learning Yoshua Bengio, Learning Deep Architectures for AI, Foundations and Trends in Machine Learning, 2(1), pp.1-127, 2009. Yoshua Bengio, Aaron Courville, Pascal Vincent, Representation Learning: A Review and New Perspectives, Arxiv, 2012. Jurgen Schmidhuber, De
Getting Started¶ These tutorials do not attempt to make up for a graduate or undergraduate course in machine learning, but we do make a rapid overview of some important concepts (and notation) to make sure that we’re on the same page. You’ll also need to download the datasets mentioned in this chapter in order to run the example code of the up-coming tutorials. Download¶ On each learning algorithm
© Copyright 2008--2010, LISA lab. Last updated on Jun 15, 2018. Created using Sphinx 1.5.
List of reading lists and survey papers: Books Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation. Review Papers Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012. The monograph or review paper Learning Deep Architectures for AI (Foundations & Trends in Machine Learning, 2009). Deep Machine Lear
Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression and Multilayer Perceptron. Additionally it uses the following Theano functions and concepts: T.tanh, shared variables, basic arithmetic ops, T.grad, Random numbers, floatX and scan. If you intend to run the code on GPU also read GPU. Energy-Based Models (EBM)¶ Energy-based models associ
Deep Learning Tutorials¶ Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of represen
Note This section assumes the reader has already read through Classifying MNIST digits using Logistic Regression. Additionally, it uses the following new Theano functions and concepts: T.tanh, shared variables, basic arithmetic ops, T.grad, L1 and L2 regularization, floatX. If you intend to run the code on GPU also read GPU. The next architecture we are going to present using Theano is the single-
Welcome¶ Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. efficient symbolic differentiation – Theano does your d
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. This website is intended to host a variety of resources and pointers to information about Deep Learning. In these pages you will find a reading list, links to software, datasets, a list of deep learning resea
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