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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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How to set class weights for imbalanced classes in Keras?

I know that there is a possibility in Keras with the class_weights parameter dictionary at fitting, but I couldn't find any example. Would somebody so kind to ...
Hendrik's user avatar
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198 votes
5 answers
152k 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 ...
tejaskhot's user avatar
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198 votes
6 answers
366k 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:
Muhammad Ali's user avatar
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180 votes
6 answers
183k views

When to use GRU over LSTM?

The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU ...
Sayali Sonawane's user avatar
178 votes
21 answers
252k 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 ...
Martin Thoma's user avatar
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114 votes
11 answers
126k views

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 ...
ragingSloth's user avatar
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89 votes
1 answer
88k 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 ...
Rizky Luthfianto's user avatar
86 votes
8 answers
66k views

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....
ahajib's user avatar
  • 1,075
83 votes
5 answers
49k 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)...
Aamir 's user avatar
  • 993
78 votes
6 answers
162k views

What is the difference between Gradient Descent and Stochastic Gradient Descent?

What is the difference between Gradient Descent and Stochastic Gradient Descent? I am not very familiar with these, can you describe the difference with a short example?
Developer's user avatar
  • 1,099
75 votes
6 answers
150k views

Cross-entropy loss explanation

Suppose I build a neural network for classification. The last layer is a dense layer with Softmax activation. I have five different classes to classify. Suppose for a single training example, the <...
enterML's user avatar
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69 votes
11 answers
102k views

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 ...
Green Falcon's user avatar
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69 votes
5 answers
51k views

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 ...
Rjay155's user avatar
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67 votes
4 answers
77k 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....
Hendrik's user avatar
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67 votes
5 answers
30k views

In softmax classifier, why use exp function to do normalization?

Why use softmax as opposed to standard normalization? In the comment area of the top answer of this question, @Kilian Batzner raised 2 questions which also confuse me a lot. It seems no one gives an ...
Hans's user avatar
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65 votes
5 answers
199k views

How to get accuracy, F1, precision and recall, for a keras model?

I want to compute the precision, recall and F1-score for my binary KerasClassifier model, but don't find any solution. Here's my actual code: ...
ZelelB's user avatar
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64 votes
4 answers
78k 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 ...
hipoglucido's user avatar
  • 1,170
60 votes
5 answers
78k 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 ...
wabbit's user avatar
  • 1,307
60 votes
3 answers
27k views

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
  • 723
51 votes
3 answers
57k views

What does Logits in machine learning mean?

"One common mistake that I would make is adding a non-linearity to my logits output." What does the term "logit" means here or what does it represent ?
Rajat's user avatar
  • 1,077
51 votes
7 answers
44k views

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
  • 1,615
48 votes
4 answers
33k 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 ...
Bunny Rabbit's user avatar
48 votes
3 answers
104k views

What is Ground Truth

In the context of Machine Learning, I have seen the term Ground Truth used a lot. I have searched a lot and found the following definition in Wikipedia: In machine learning, the term "ground ...
Green Falcon's user avatar
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47 votes
5 answers
69k views

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 ...
wit221's user avatar
  • 573
47 votes
4 answers
32k views

Early stopping on validation loss or on accuracy?

I am currently training a neural network and I cannot decide which to use to implement my Early Stopping criteria: validation loss or a metrics like accuracy/f1score/auc/whatever calculated on the ...
qmeeus's user avatar
  • 1,279
46 votes
2 answers
75k views

Merging two different models in Keras

I am trying to merge two Keras models into a single model and I am unable to accomplish this. For example in the attached Figure, I would like to fetch the middle layer $A2$ of dimension 8, and use ...
Rkz's user avatar
  • 1,033
44 votes
6 answers
61k views

What is the relationship between the accuracy and the loss in deep learning?

I have created three different models using deep learning for multi-class classification and each model gave me a different accuracy and loss value. The results of the testing model as the following: ...
N.IT's user avatar
  • 1,995
44 votes
2 answers
68k views

Should we apply normalization to test data as well?

I am doing a project on an author identification problem. I applied the tf-idf normalization to train data and then trained an SVM on that data. Now when using the classifier, should I normalize test ...
Kishan Kumar's user avatar
44 votes
4 answers
57k 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 GPUs and you would like to use all of them ...
Hector Blandin's user avatar
44 votes
5 answers
42k views

Intuitive explanation of Noise Contrastive Estimation (NCE) loss?

I read about NCE (a form of candidate sampling) from these two sources: Tensorflow writeup Original Paper Can someone help me with the following: A simple explanation of how NCE works (I found the ...
tejaskhot's user avatar
  • 4,065
43 votes
13 answers
27k views

Data science related funny quotes [closed]

It has been customary for the users of different communities to quote funny things about their fields. It may be fun to share your funny things about Machine Learning, Deep Learning, Data Science and ...
42 votes
5 answers
55k views

In the context of Deep Learning, what is training warmup steps

I found the term "training warmup steps" in some of the papers. What exactly does this term mean? Has it got anything to do with "learning rate"? If so, how does it affect it?
Ashwin Geet D'Sa's user avatar
41 votes
6 answers
53k 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 ...
Green Falcon's user avatar
  • 14.1k
41 votes
6 answers
11k 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 ...
stk1234's user avatar
  • 583
41 votes
3 answers
50k views

Choosing between CPU and GPU for training a neural network

I've seen discussions about the 'overhead' of a GPU, and that for 'small' networks, it may actually be faster to train on a CPU (or network of CPUs) than a GPU. What is meant by 'small'? For ...
StatsSorceress's user avatar
39 votes
1 answer
57k 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 ...
pseudomonas's user avatar
  • 1,042
39 votes
2 answers
106k views

Keras difference beetween val_loss and loss during training

What is the difference between val_loss and loss during training in Keras? E.g. ...
Vladimir Shebuniayeu's user avatar
36 votes
6 answers
16k views

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 ...
Praise the lord's user avatar
36 votes
1 answer
16k views

Time Series prediction using LSTMs: Importance of making time series stationary

In this link on Stationarity and differencing, it has been mentioned that models like ARIMA require a stationarized time series for forecasting as it's statistical properties like mean, variance, ...
PixelPioneer's user avatar
36 votes
1 answer
13k views

Paper: What's the difference between Layer Normalization, Recurrent Batch Normalization (2016), and Batch Normalized RNN (2015)?

So, recently there's a Layer Normalization paper. There's also an implementation of it on Keras. But I remember there are papers titled Recurrent Batch Normalization (Cooijmans, 2016) and Batch ...
Rizky Luthfianto's user avatar
34 votes
4 answers
79k views

How to use LeakyRelu as activation function in sequence DNN in keras?When it perfoms better than Relu?

How do you use LeakyRelu as an activation function in sequence DNN in keras? If I want to write something similar to: ...
user10296606's user avatar
  • 1,834
34 votes
2 answers
40k views

Are there any rules for choosing the size of a mini-batch?

When training neural networks, one hyperparameter is the size of a minibatch. Common choices are 32, 64, and 128 elements per mini batch. Are there any rules/guidelines on how big a mini-batch should ...
Martin Thoma's user avatar
  • 18.9k
34 votes
4 answers
17k views

Gumbel-Softmax trick vs Softmax with temperature

From what I understand, the Gumbel-Softmax trick is a technique that enables us to sample discrete random variables, in a way that is differentiable (and therefore suited for end-to-end deep learning)....
4-bit's user avatar
  • 441
31 votes
7 answers
19k views

Can machine learning learn a function like finding maximum from a list?

I have an input which is a list and the output is the maximum of the elements of the input-list. Can machine learning learn such a function which always selects the maximum of the input-elements ...
user78739's user avatar
  • 319
30 votes
1 answer
27k views

What is the difference between upsampling and bi-linear upsampling in a CNN?

I am trying to understand this paper and am unsure of what bi-linear upsampling is. Can anyone explain this at a high-level?
JGG's user avatar
  • 513
29 votes
6 answers
10k 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 ?
Maxi's user avatar
  • 433
29 votes
2 answers
30k views

What is the difference between fit() and fit_generator() in Keras?

What is the difference between fit() and fit_generator() in Keras? When should I use fit() ...
N.IT's user avatar
  • 1,995
28 votes
2 answers
35k views

What is the advantage of using log softmax instead of softmax?

Are there any advantages to using log softmax over softmax? What are the reasons to choose one over the other?
rawwar's user avatar
  • 861
28 votes
3 answers
55k views

What is weight and bias in deep learning?

I'm starting to learn Machine learning from Tensorflow website. I have developed a very very rudimentary understanding of the flow a deep learning program follows (this method makes me learn fast ...
Umer Farooq's user avatar
28 votes
3 answers
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.
user3352632's user avatar

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