Questions tagged [deep-learning]

a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.

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Do I need to buy a NVIDIA graphic card to run deep learning algorithm?

I am new in deep learning. I am running a MacBook Pro yosemite (upgraded from Snowleopard). I don't have a CUDA-enabled card GPU, and running the code on the CPU is extremely slow. I heard that I can ...
Lilianna's user avatar
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"Recursive ConvNets for Dummies" Library [closed]

I've look at the questions on here regarding the different python libraries around for deep learning and neural nets. They include: Keras Caffe Lasagne PyLearn2 Deepy Theano Torch My understanding ...
Elliott Rogasik's user avatar
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Fisher's Iris data set with Caffe

I am trying to use Caffe on the usual Fisher's Iris data set (150 flowers, each having 4 features, and split into 3 classes): if a flower belong to class 1 (setosa), the network output should be [1, ...
Franck Dernoncourt's user avatar
5 votes
1 answer

How to do multitask learning using Caffe?

I wonder how to do multitask learning using Caffe. Should I simply use the output layer SigmoidCrossEntropyLoss or EuclideanLoss, and define more than one outputs? E.g. is the following architecture ...
Franck Dernoncourt's user avatar
5 votes
2 answers

Can theano work on mapreduce or on spark?

I am not sure whether the Theano library can be used to write parallelized code in map reduce or in spark. Any expert opinion is welcome. A discussion was on at: Theano-dev
0xF's user avatar
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Caffe net.predict() outputs random results (GoogleNet)

I used pretrained GoogleNet from and finetuned it with my own data (~ 100k images, 101 classes). After one day training I achieved 62% ...
Rachnog's user avatar
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-1 votes
2 answers

How can an undergraduate learn more about deep learning? [duplicate]

I am an undergraduate who needs to submit a thesis for graduation. I am fairly interested in deep learning, and am working on a project that uses deep learning methods extensively (rCNNs to be precise)...
Rohit's user avatar
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10 votes
1 answer

How do I calculate the delta term of a Convolutional Layer, given the delta terms and weights of the previous Convolutional Layer?

I am trying to train an artificial neural network with two convolutional layers (c1, c2) and two hidden layers (c1, c2). I am using the standard backpropagation approach. In the backward pass I ...
cdwoelk's user avatar
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9 votes
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Theano in deep learning research

How widely is Theano used in deep learning research? Is Theano a good start to learn the implementation of machine learning algorithms? Will learning the implementation of something like a feed ...
user avatar
6 votes
2 answers

Convolutional neural network for sparse one-hot representation

I have some basic features which I encoded in a one-hot vector. Length of the feature vector equals to 400. It is sparse. I saw that conv nets is applied to a dense feature vectors. Is there any ...
nub's user avatar
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198 votes
5 answers

What is the "dying ReLU" problem in neural networks?

Referring to the Stanford course notes on Convolutional Neural Networks for Visual Recognition, a paragraph says: "Unfortunately, ReLU units can be fragile during training and can "die". For ...
tejaskhot's user avatar
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Prove Reccurrent Neural Network can exhibit oscillatory behavior

I understand how recurrent neural networks work, however I'm trying to build a deep intuitive understanding of their behavior which is difficult for me because they exhibit such complex behaviors. ...
Dr.Knowitall's user avatar
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Identity covariance matrix, decorrelated data?

Why would you want to decorrelated data? As I am reading about PCA and whitening on image data for DNN, I wonder what is the purpose of achieving the identity covariance matrix in your data is? Is ...
user3711518's user avatar
2 votes
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Which one is easier to debug between theano and caffe? [closed]

I am investigating whether to use theano or caffe for convnets. I would like to know which one provides a better debug environment. In caffe, it seems you don't write any code just a config in ...
morpheus's user avatar
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3 votes
1 answer

What are the possible ways to handle class unbalance in a large scale image recognition problem with Deep Neural Nets?

I have 22 classes of objects but they have very skewed distributions where max class has 100.000 images and the min class has 1600 images. In that setting I would like to hear some possible solutions ...
erogol's user avatar
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Route to picking up Deep learning [closed]

I would like to pick up on the topic of deep learning. Should I begin from the topic of AI before working my way into Deep learning?
zen's user avatar
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10 votes
3 answers

What cost function and penalty are suitable for imbalanced datasets?

For an imbalanced data set, is it better to choose an L1 or L2 regularization? Is there a cost function more suitable for imbalanced datasets to improve the model score (...
red_GNS's user avatar
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15 votes
2 answers

Visualizing deep neural network training

I'm trying to find an equivalent of Hinton Diagrams for multilayer networks to plot the weights during training. The trained network is somewhat similar to a Deep SRN, i.e. it has a high number of ...
runDOSrun's user avatar
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29 votes
6 answers

Deep learning basics

I am looking for a paper detailing the very basics of deep learning. Ideally like the Andrew Ng course for deep learning. Do you know where I can find this ?
Maxi's user avatar
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51 votes
7 answers

Deep Learning vs gradient boosting: When to use what?

I have a big data problem with a large dataset (take for example 50 million rows and 200 columns). The dataset consists of about 100 numerical columns and 100 categorical columns and a response column ...
Nitesh's user avatar
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28 votes
3 answers

Why are NLP and Machine Learning communities interested in deep learning?

I hope you can help me, as I have some questions on this topic. I'm new in the field of deep learning, and while I did some tutorials, I can't relate or distinguish concepts from one another.
user3352632's user avatar
60 votes
3 answers

How to fight underfitting in a deep neural net

When I started with artificial neural networks (NN) I thought I'd have to fight overfitting as the main problem. But in practice I can't even get my NN to pass the 20% error rate barrier. I can't even ...
lithuak's user avatar
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114 votes
11 answers

Choosing a learning rate

I'm currently working on implementing Stochastic Gradient Descent, SGD, for neural nets using back-propagation, and while I understand its purpose I have some ...
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