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Questions tagged [machine-learning]

Methods and principles of building "computer systems that automatically improve with experience."

121
votes
17answers
90k views

Best python library for neural networks

I'm using Neural Networks to solve different Machine learning problems. I'm using Python and pybrain but this library is almost discontinued. Are there other good alternatives in Python?
90
votes
15answers
93k views

Python vs R for machine learning

I'm just starting to develop a machine learning application for academic purposes. I'm currently using R and training myself in it. However, in a lot of places, I saw people using Python. What are ...
86
votes
4answers
96k views

The cross-entropy error function in neural networks

In the MNIST For ML Beginners they define cross-entropy as $$H_{y'} (y) := - \sum_{i} y_{i}' \log (y_i)$$ $y_i$ is the predicted probability value for class $i$ and $y_i'$ is the true probability ...
77
votes
5answers
44k 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 ...
75
votes
8answers
79k 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 ...
56
votes
5answers
29k views

Why do cost functions use the square error?

I'm just getting started with some machine learning, and until now I have been dealing with linear regression over one variable. I have learnt that there is a hypothesis, which is: $h_\theta(x)=\...
51
votes
7answers
20k views

Data scientist vs machine learning engineer

What are the differences, if any, between a "data scientist" and a "machine learning engineer"? Over the past year or so "machine learning engineer" has started to show up a lot in job postings. ...
49
votes
8answers
61k views

Open source Anomaly Detection in Python

Problem Background: I am working on a project that involves log files similar to those found in the IT monitoring space (to my best understanding of IT space). These log files are time-series data, ...
48
votes
12answers
36k 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 ...
47
votes
6answers
62k views

strings as features in decision tree/random forest

I am new to machine learning! Right now I am doing some problems on an application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country ...
46
votes
8answers
9k views

Why Is Overfitting Bad in Machine Learning?

Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does ...
46
votes
7answers
55k views

Gini Impurity vs Entropy

Can someone practically explain the rationale behind Gini impurity vs Information gain (based on Entropy)? Which metric is better to use in different scenarios while using decision trees?
43
votes
3answers
31k views

Advantages of AUC vs standard accuracy

I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I've ...
40
votes
9answers
11k views

Is there any domain where Bayesian Networks outperform neural networks?

Neural networks get top results in Computer Vision tasks (see MNIST, ILSVRC, Kaggle Galaxy Challenge). They seem to outperform every other approach in Computer Vision. But there are also other tasks: ...
40
votes
4answers
39k views

Neural networks: which cost function to use?

I am using TensorFlow for experiments mainly with neural networks. Although I have done quite some experiments (XOR-Problem, MNIST, some Regression stuff, ...) now, I struggle with choosing the "...
39
votes
5answers
8k views

Should I go for a 'balanced' dataset or a 'representative' dataset?

My 'machine learning' task is of separating benign Internet traffic from malicious traffic. In the real world scenario, most (say 90% or more) of Internet traffic is benign. Thus I felt that I should ...
38
votes
8answers
36k views

Clustering geo location coordinates (lat,long pairs)

What is the right approach and clustering algorithm for geolocation clustering? I'm using the following code to cluster geolocation coordinates: ...
37
votes
10answers
40k views

Can machine learning algorithms predict sports scores or plays?

I have a variety of NFL datasets that I think might make a good side-project, but I haven't done anything with them just yet. Coming to this site made me think of machine learning algorithms and I ...
37
votes
2answers
52k views

RNN vs CNN at a high level

I've been thinking about the Recurrent Neural Networks (RNN) and their varieties and Convolutional Neural Networks (CNN) and their varieties. Would these two points be fair to say: Use CNNs to break ...
36
votes
10answers
14k views

Why are Machine Learning models called black boxes?

I was reading this blog post titled: The Financial World Wants to Open AI’s Black Boxes, where the author repeatedly refer to ML models as "black boxes". A similar terminology has been used at ...
36
votes
3answers
43k 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:
35
votes
11answers
35k views

Data Science in C (or C++)

I'm an R language programmer. I'm also in the group of people who are considered Data Scientists but who come from academic disciplines other than CS. This works ...
34
votes
3answers
2k views

When to use what - Machine Learning [closed]

Recently in a Machine Learning class from professor Oriol Pujol at UPC/Barcelona he described the most common algorithms, principles and concepts to use for a wide range of machine learning related ...
32
votes
10answers
24k views

Machine learning - features engineering from date/time data

What are the common/best practices to handle time data for machine learning application? For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can ...
30
votes
5answers
9k views

What are some standard ways of computing the distance between documents?

When I say "document", I have in mind web pages like Wikipedia articles and news stories. I prefer answers giving either vanilla lexical distance metrics or state-of-the-art semantic distance metrics,...
30
votes
4answers
31k views

GBM vs XGBOOST? Key differences?

I am trying to understand the key difference between GBM and XGBOOST. I tried to google it but could not find any good answer explaining the difference between the two algos and why does xgboost ...
29
votes
1answer
9k views

The difference between `Dense` and `TimeDistributedDense` of `Keras`

I am still confused about the difference between Dense and TimeDistributedDense of Keras ...
28
votes
5answers
15k 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 ...
28
votes
3answers
12k 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 ...
27
votes
4answers
21k 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....
27
votes
4answers
9k views

Quick guide into training highly imbalanced data sets

I have a classification problem with approximately 1000 positive and 10000 negative samples in training set. So this data set is quite unbalanced. Plain random forest is just trying to mark all test ...
26
votes
6answers
3k views

Machine learning techniques for estimating users' age based on Facebook sites they like

I have a database from my Facebook application and I am trying to use machine learning to estimate users' age based on what Facebook sites they like. There are three crucial characteristics of my ...
26
votes
4answers
20k views

What algorithms should I use to perform job classification based on resume data?

Note that I am doing everything in R. The problem goes as follow: Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don't. The goal here is to: ...
25
votes
5answers
11k views

Difference between AlphaGo's policy network and value network

I was reading a high level summary about Google's AlphaGo (http://googleresearch.blogspot.co.uk/2016/01/alphago-mastering-ancient-game-of-go.html), and I came across the terms "policy network" and "...
23
votes
5answers
14k views

In supervised learning, why is it bad to have correlated features?

I read somewhere that if we have features that are too correlated, we have to remove one, as this may worsen the model. It is clear that correlated features means that they bring the same information, ...
23
votes
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.
23
votes
4answers
9k views

Word2Vec for Named Entity Recognition

I'm looking to use google's word2vec implementation to build a named entity recognition system. I've heard that recursive neural nets with back propagation through structure are well suited for named ...
22
votes
6answers
40k views

Train/Test/Validation Set Splitting in Sklearn

How could I split randomly a data matrix and the corresponding label vector into a X_train, X_test, X_val, y_train, y_test, y_val with Sklearn? As far as I know, ...
22
votes
3answers
40k views

Data Science Project Ideas [closed]

I don't know if this is a right place to ask this question, but a community dedicated to Data Science should be the most appropriate place in my opinion. I have just started with Data Science and ...
21
votes
6answers
7k views

Deep learning basics [duplicate]

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 ?
21
votes
3answers
25k views

General approach to extract key text from sentence (nlp)

Given a sentence like: Complimentary gym access for two for the length of stay ($12 value per person per day) What general approach can I take to identify the ...
21
votes
3answers
15k views

Do Random Forest overfit?

I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when ...
19
votes
7answers
2k views

Purpose of visualizing high dimensional data?

There are many techniques for visualizing high dimension datasets, such as T-SNE, isomap, PCA, supervised PCA, etc. And we go through the motions of projecting the data down to a 2D or 3D space, so we ...
19
votes
4answers
12k 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 normalizing is clear and is for having same range of feature values, but after struggling a lot I did not find ...
19
votes
4answers
6k views

Meaning of latent features?

I am learning about matrix factorization for recommender systems and I am seeing the term latent features occurring too frequently but I am unable to understand ...
19
votes
1answer
128 views

What is meant by sharing of parameters between features and classes

When reading this paper there is a line which says "linear classifiers do not share parameters among features and classes." What is the meaning of this statement? Does it mean that linear ...
18
votes
6answers
17k 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 ...
18
votes
3answers
9k views

Why do we need XGBoost and Random Forest?

I wasn't clear on couple of concepts: XGBoost converts weak learners to strong learners. What's the advantage of doing this ? Combining many weak learners instead of just using a single tree ? ...
18
votes
3answers
21k views

When to use Random Forest over SVM and vice versa?

When would one use Random Forest over SVM and vice versa? I understand that ...
18
votes
3answers
9k views

How to perform feature engineering on unknown features?

I am participating on a kaggle competition. The dataset has around 100 features and all are unknown (in terms of what actually they represent). Basically they are just numbers. People are performing ...