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Understanding Convolutional Neural Networks for NLP

Understanding Convolutional Neural Networks for NLP
When we hear about Convolutional Neural Network (CNNs), we typically think of Computer Vision. CNNs were responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today, from Facebook’s automated photo tagging to self-driving cars. More recently we’ve also started to apply CNNs to problems in Natural Language Processing and gotten some interesting results. What is Convolution? The for me easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix. Convolution with 3×3 Filter. Imagine that the matrix on the left represents an black and white image. You may be wondering wonder what you can actually do with this. Averaging each pixel with its neighboring values blurs an image: Taking the difference between a pixel and its neighbors detects edges: The GIMP manual has a few other examples. What are Convolutional Neural Networks? So, how does any of this apply to NLP? CNN Hyperparameters Narrow vs.

Exercising Sparse Autoencoder | Vanessa's Imiloa Home > Computer Vision, Machine Learning > Exercising Sparse Autoencoder Deep learning recently becomes such a hot topic across both academic and industry area. Guess the best way to learn some stuff is to implement them. ACL 2012 + NAACL 2013 Tutorial: Deep Learning for NLP (without Magic) and they have a nice ‘assignment‘ for whoever wants to learn for sparse autoencoder. There are two main parts for an autoencoder: feedforward and backpropagation. You can think of autoencoder as an unsupervised learning algorithm, that sets the target value to be equal to the inputs. Thus, the network will be forced to learn a compressed representation of the input. After learning completed, the weights represent the signals ( think of certain abstraction or atoms) that unsupervised learned from the data, like below: Like this: Like Loading...

Understanding Natural Language with Deep Neural Networks Using Torch This post was co-written by Soumith Chintala and Wojciech Zaremba of Facebook AI Research. Language is the medium of human communication. Giving machines the ability to learn and understand language enables products and possibilities that are not imaginable today. One can understand language at varying granularities. As an example, language understanding gives one the ability to understand that the sentences “I’m on my way home.” and “I’m driving back home.” both convey that the speaker is going home. Word Maps and Language Models For a machine to understand language, it first has to develop a mental map of words, their meanings and interactions with other words. Word embeddings can either be learned in a general-purpose fashion before-hand by reading large amounts of text (like Wikipedia), or specially learned for a particular task (like sentiment analysis). An even simpler metric is to predict the next word in the sentence. “I am eating _____” “I am eating an apple.” Learn More at GTC 2015

Fast Forward Labs: How do neural networks learn? Neural networks are generating a lot of excitement, as they are quickly proving to be a promising and practical form of machine intelligence. At Fast Forward Labs, we just finished a project researching and building systems that use neural networks for image analysis, as shown in our toy application Pictograph. Our companion deep learning report explains this technology in depth and explores applications and opportunities across industries. As we built Pictograph, we came to appreciate just how challenging it is to understand how neural networks work. To help understand how neural networks learn, I built a visualization of a network at the neuron level, including animations that show how it learns. Neural Network Basics First, some deep learning basics. Neural networks generally perform supervised learning tasks, building knowledge from data sets where the right answer is provided in advance. To do this, the network compares initial outputs with a provided correct answer, or target.

District Data Labs - Modern Methods for Sentiment Analysis Modern Methods for Sentiment Analysis Michael Czerny Sentiment analysis is a common application of Natural Language Processing (NLP) methodologies, particularly classification, whose goal is to extract the emotional content in text. In this way, sentiment analysis can be seen as a method to quantify qualitative data with some sentiment score. The simplest form of sentiment analysis is to use a dictionary of good and bad words. Another common method is to treat a text as a “bag of words”. Word2Vec and Doc2Vec Recently, Google developed a method called Word2Vec that captures the context of words, while at the same time reducing the size of the data. Figure 1: Architecture for the CBOW and Skip-gram method, taken from Efficient Estimation of Word Representations in Vector Space. These word vectors now capture the context of surrounding words. However, even with the above method of averaging word vectors, we are ignoring word order. Word2Vec Example in Python "ate" - "eat" + "speak" = "spoke"

Going Deeper into Neural Networks Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software EngineerUpdate - 13/07/2015Images in this blog post are licensed by Google Inc. under a Creative Commons Attribution 4.0 International License. However, images based on places by MIT Computer Science and AI Laboratory require additional permissions from MIT for use.Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. So here’s one surprise: neural networks that were trained to discriminate between different kinds of images have quite a bit of the information needed to generate images too. Why is this important? Of course, we can do more than cloud watching with this technique.

Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. Neural networks approach the problem in a different way. and then develop a system which can learn from those training examples. In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. Perceptrons What is a neural network? So how do perceptrons work? is a shorthand.

WildML | A blog about Machine Learning, Deep Learning and NLP. NaturalNode/natural Composing Music With Recurrent Neural Networks | hexahedria (Update: A paper based on this work has been accepted at EvoMusArt 2017! See here for more details.) It’s hard not to be blown away by the surprising power of neural networks these days. With enough training, so called “deep neural networks”, with many nodes and hidden layers, can do impressively well on modeling and predicting all kinds of data. (If you don’t know what I’m talking about, I recommend reading about recurrent character-level language models, Google Deep Dream, and neural Turing machines. Very cool stuff!) For a while now, I’ve been floating around vague ideas about writing a program to compose music. Here’s a taste of things to come: But first, some background about neural networks, and RNNs in particular. Feedforward Neural Networks: A single node in a simple neural network takes some number of inputs, and then performs a weighted sum of those inputs, multiplying them each by some weight before adding them all together. A sigmoid function. Recurrent Neural Networks Results

Building a deeper understanding of images Posted by Christian Szegedy, Software Engineer The ImageNet large-scale visual recognition challenge (ILSVRC) is the largest academic challenge in computer vision, held annually to test state-of-the-art technology in image understanding, both in the sense of recognizing objects in images and locating where they are. Participants in the competition include leading academic institutions and industry labs. In 2012 it was won by DNNResearch using the convolutional neural network approach described in the now-seminal paper by Krizhevsky et al.[4] In this year’s challenge, team GoogLeNet (named in homage to LeNet, Yann LeCun's influential convolutional network) placed first in the classification and detection (with extra training data) tasks, doubling the quality on both tasks over last year's results. The competition has three tracks: classification, classification with localization, and detection.

What a Deep Neural Network thinks about your #selfie Convolutional Neural Networks are great: they recognize things, places and people in your personal photos, signs, people and lights in self-driving cars, crops, forests and traffic in aerial imagery, various anomalies in medical images and all kinds of other useful things. But once in a while these powerful visual recognition models can also be warped for distraction, fun and amusement. In this fun experiment we're going to do just that: We'll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones. Yeah, I'll do real work. Convolutional Neural Networks Before we dive in I thought I should briefly describe what Convolutional Neural Networks (or ConvNets for short) are in case a slightly more general audience reader stumbles by. Few of many examples of ConvNets being useful. A bit of history. Under the hood So how do they work? Training. Training a ConvNet

Open-source, distributed deep learning for the JVM Contents Definition & Structure Invented by Geoff Hinton, Restricted Boltzmann machines are useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. (For more concrete examples of how neural networks like RBMs can be employed, please see our page on use cases). Given their relative simplicity, restricted Boltzmann machines are the first neural network we’ll tackle. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. Each circle in the graph above represents a neuron-like unit called a node, and nodes are simply where calculations take place. That is, there is no intra-layer communication – this is the restriction in a restricted Boltzmann machine. Each visible node takes a low-level feature from an item in the dataset to be learned. Now let’s follow that single pixel value, x, through the two-layer net. activation f((weight w * input x) + bias b ) = output a iris.scale();

Machine Learning Blog: Autoencoders

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