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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26
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5answers
4k views

How to set the number of neurons and layers in neural networks

I am a beginner to neural networks and have had trouble grasping two concepts: How does one decide the number of middle layers a given neural network have? 1 vs. 10 or whatever. How does one decide ...
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4answers
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Does gradient descent always converge to an optimum?

I am wondering whether there is any scenario in which gradient descent does not converge to a minimum. I am aware that gradient descent is not always guaranteed to converge to a global optimum. I am ...
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6answers
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Why do convolutional neural networks work?

I have often heard people saying that why convolutional neural networks are still poorly understood. Is it known why convolutional neural networks always end up learning increasingly sophisticated ...
123
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5answers
68k views

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 ...
43
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4answers
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Adding Features To Time Series Model LSTM

have been reading up a bit on LSTM's and their use for time series and its been interesting but difficult at the same time. One thing I have had difficulties with understanding is the approach to ...
43
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3answers
35k views

Number of parameters in an LSTM model

How many parameters does a single stacked LSTM have? The number of parameters imposes a lower bound on the number of training examples required and also influences the training time. Hence knowing the ...
42
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4answers
38k views

Why mini batch size is better than one single “batch” with all training data?

I often read that in case of Deep Learning models the usual practice is to apply mini batches (generally a small one, 32/64) over several training epochs. I cannot really fathom the reason behind this....
38
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3answers
12k views

What is the difference between “equivariant to translation” and “invariant to translation”

I'm having trouble understanding the difference between equivariant to translation and invariant to translation. In the book Deep Learning. MIT Press, 2016 (I. Goodfellow, A. Courville, and Y. Bengio)...
11
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1answer
3k views

Using a pre trained CNN classifier and apply it on a different image dataset

How would you optimize a pre-trained neural network to apply it to a separate problem? Would you just add more layers to the pre-trained model and test it on your ...
10
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1answer
14k views

Keras LSTM with 1D time series

I'm learning how to use Keras and I've had reasonable success with my labelled dataset using the examples on Chollet's Deep Learning for Python. The data set is ~1000 Time Series with length 3125 with ...
3
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1answer
4k views

What are useful evaluation metrics used in machine learning

I am using CNN in order to predict codes after analyzing text. As an example, I will write "I am crazy" .. the model will predict some code " X321". All this based on CNN. I want to evaluate my ...
7
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1answer
10k views

Why should softmax be used in CNN

In the last layer of CNNs and MLPs it is common to use softmax layer or units with sigmoid activation functions for multi-class ...
86
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15answers
68k views

How do you visualize neural network architectures?

When writing a paper / making a presentation about a topic which is about neural networks, one usually visualizes the networks architecture. What are good / simple ways to visualize common ...
22
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6answers
8k views

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 ?
66
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5answers
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Time series prediction using ARIMA vs LSTM

The problem that I am dealing with is predicting time series values. I am looking at one time series at a time and based on for example 15% of the input data, I would like to predict its future values....
53
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2answers
48k views

When to use (He or Glorot) normal initialization over uniform init? And what are its effects with Batch Normalization?

I knew that Residual Network (ResNet) made He normal initialization popular. In ResNet, He normal initialization is used , while the first layer uses He uniform initialization. I've looked through ...
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7answers
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Why should the data be shuffled for machine learning tasks

In machine learning tasks it is common to shuffle data and normalize it. The purpose of normalization is clear (for having same range of feature values). But, after struggling a lot, I did not find ...
17
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1answer
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Why ReLU is better than the other activation functions

Here the answer refers to vanishing and exploding gradients that has been in sigmoid-like activation functions but, I guess, Relu...
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2answers
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Should we apply normalization to test data as well?

I am doing a project on author identification problem. I had applied the tf-idf normalization to train data and then trained a svm on that data. Now when using the classifier should I normalize test ...
17
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2answers
4k views

local minima vs saddle points in deep learning

I heard Andrew Ng (in a video I unfortunately can't find anymore) talk about how the understanding of local minima in deep learning problems has changed in the sense that they are now regarded as less ...
4
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1answer
20k views

How to download dynamic files created during work on Google Colab?

I have two different files and on the first, I tried to save data to file as: np.save(open(Q1_TRAINING_DATA_FILE, 'wb'), q1_data) On second file, i'm trying to ...
12
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1answer
17k views

What is a 1D Convolutional Layer in Deep Learning?

I have a good general understanding of the role and mechanism of convolutional layers in Deep Learning for image processing in case of 2D or 3D implementations - they "simply" try to catch 2D patterns ...
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2answers
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How to plot cost versus number of iterations in scikit learn?

One of the recommendations in the Coursera Machine Learning course when working with gradient descent based algorithms is: Debugging gradient descent. Make a plot with number of iterations on the x-...
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2answers
648 views

Explain Binary Classification with output 0.5 (True)

What is the interpretation of output 0.5 of a typical classifier? I made a prediction and the probability of that data point being from the True class is 0.5.
87
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5answers
105k views

How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
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3answers
2k views

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.
39
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3answers
36k views

Does batch_size in Keras have any effects in results' quality?

I am about to train a big LSTM network with 2-3 million articles and am struggling with Memory Errors (I use AWS EC2 g2x2large). I found out that one solution is to reduce the ...
19
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2answers
11k views

Why is ReLU used as an activation function?

Activation functions are used to introduce non-linearities in the linear output of the type w * x + b in a neural network. Which I am able to understand ...
11
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2answers
587 views

When do we say that the dataset is not classifiable?

I have many times analysed a dataset on which I could not really do any sort of classification. To see whether I can get a classifier I have usually used the following steps: Generate box plots of ...
10
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2answers
22k views

Question about bias in Convolutional Networks

I am trying to figure out how many weights and biases are needed for CNN. Say I have a (3, 32, 32)-image and want to apply a (32, 5, 5)-filter. For each feature map I have 5x5 weights, so I should ...
12
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4answers
3k views

Why does adding a dropout layer improve deep/machine learning performance, given that dropout suppresses some neurons from the model?

If removing some neurons results in a better performing model, why not use a simpler neural network with fewer layers and fewer neurons in the first place? Why build a bigger, more complicated model ...
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4answers
4k views

How does deep learning helps in detecting multiple objects in single image?

Let's say there are two cars in an image. How can it detect these cars, given that it can detect single car in an image?
3
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1answer
235 views

ANN on Pattern Recognition

I have been trying to apply a simple neural network using keras to predict a sequence of numbers and the rule is if the input integer is odd it should be 4 and if its even it should be 2. Yet the ...
2
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1answer
267 views

Compute backpropagation

I have the question which is mentioned in the above picture. It is trying to find the derivative of f with respect to weight matrix ...
11
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1answer
4k views

Reason for square images in deep learning

Most of the advanced deep learning models like VGG, ResNet, etc. require square images as input, usually with a pixel size of $224x224$. Is there a reason why the input has to be of equal shape, or ...
7
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1answer
993 views

After the training phase, is it better to run neural networks on a GPU or CPU?

My understanding is that GPUs are more efficient for running neural nets, but someone recently suggested to me that GPUs are only needed for the training phase. Once trained, it's actually more ...
4
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2answers
535 views

What does it mean for the training data to be generated by a probability distribution over datasets

I was reading the Deep Learning book and came across the following para (page 109, second para): The training and test data are generated by a probability distribution over datasets called the data-...
6
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1answer
5k views

Keras: How to normalize dataframe with continuous and categorical data?

I have a dataframe with about 50 columns. The columns are either categorical or continuous data. The continuous data can be between 0.000001-1.00000 or they can be between 500,000-5,000,000. The ...
4
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2answers
1k views

How to sort numbers using Convolutional Neural Network?

Recently, in an interview I got this question: Design a convnet that sorts numbers. Operators are ReLU, Conv, and Pooling. E.g. input: 5, 3, 6, 2; output: 2, 3, 5, 6 I am not sure how can you sort a ...
3
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1answer
2k views

Example of 1D ConvNet filter

I understand Conv2D filters. I think I understand Conv1D filters as well but have not seen any examples of the filters like what ...
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1answer
78 views

Rephrase Neural Network : Find the Fittest Word for Given Meaning/Explanation

I am a sophomore student who's interested in deep learning and its method layering up some linear/non-linear operations and constructs up the complex function through the network. I'd like to ...
5
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1answer
3k views

Keras CNN with low/constant accuracies

I am dealing with the Street View House Number recognition problem. I am trying to train a CNN with Keras. Here is how I prepared the input: ...
4
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2answers
1k views

Should the bias value be added after convolution operation in CNNs?

Should we add bias to each entry of the convolution then sum, or add bias once at end of calculating the convolution in CNNs?
3
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1answer
162 views

Fine-tuning a CNN for recognizing two classes, but also being able to tell if none of them is present in an image

I need to fine-tune a CNN to classify two classes: dogs and cats, for example. However, I want the CNN to be able to tell if ...
3
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1answer
2k views

Validation loss increases and validation accuracy decreases

I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is ...
1
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1answer
70 views

Is the activation function the only difference between logistic regression and perceptron?

As far as I know, logistic regression can be denoted as: $$ f(x) = \sigma(w \cdot x + b) $$ A perceptron can be denoted as: $$ f(x) = \operatorname{sign} (w \cdot x + b) $$ It seems that the only ...
35
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3answers
35k views

Multi GPU in keras

How we can program in the keras library (or tensorflow) to partition training on multiple GPUs? Let's say that you are in an Amazon ec2 instance that has 8 GPU's and you would like to use all of them ...
27
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7answers
29k views

Are there free cloud services to train machine learning models?

I want to train a deep model with a large amount of training data, but my desktop does not have that power to train such a deep model with these abundant data. I'd like to know whether there are any ...
19
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3answers
18k views

Keyword/phrase extraction from Text using Deep Learning libraries

Perhaps this is too broad, but I am looking for references on how to use deep learning in a text summarization task. I have already implemented text summarization using standard word-frequency ...
26
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1answer
40k views

How does Keras calculate accuracy?

How does Keras calculate accuracy from the classwise probabilities? Say, for example we have 100 samples in the test set which can belong to one of two classes. We also have a list of the classwise ...