Questions tagged [reference-request]

"References" is our generic tag for questions seeking information about books, papers, presentations, videos of lectures, on-line tutorials, etc., regarding any subject matter that is on-topic for Data Science.

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How to remove outliers properly?

I was wondering what is the best practice for removing outliers from data. Plotting a boxplot for each feature (column of the dataset) and removing data that fall outside the whiskers seems like a ...
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Techniques to increase the evaluation speed of a neural network

This is somewhat of an open ended question and in some respects a literature request (I would love to be pointed to a survey paper if one exists). Suppose I am constructing a neural network to make ...
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1 vote
1 answer
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What are the possible applications of a Data Scientist in the design fase of an Aerospace Or Railway Engineering industry? [closed]

I have been trying to understand this for a long time, but this information proves to be incredibly elusive online. What are possible jobs that a pure Data Scientist, without much background knowledge,...
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0 votes
1 answer
64 views

Word Embeddings fastText in 50 dimension

Is there a fastText embedding in 50 dimensions? I'm aware of GloVe embedding is dimensions (50, 100, 200, 300) dimensions. I am trying to sentiment analysis with a very small dataset. If there is ...
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0 answers
30 views

Low dimensional manifold in a high dimensional space and Geodesic distance

It is a common assumption that high-dimensional objects are lying in low-dimensional manifolds. And this constitutes a foundation for manifold learning or dimensional reduction techniques or (a way to ...
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2 votes
1 answer
73 views

Rate of convergence - comparison of supervised ML methods

I am working on a project with sparse labelled datasets, and am looking for references regarding the rate of convergence of different supervised ML techniques with respect to dataset size. I know that ...
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3 votes
1 answer
39 views

References/tutorials about data mining and machine learning

I am learning data analytics and I wonder if there are some good references and tutorials about machine learning, data analytics and data mining? What I'm searching for is an understandable reference/...
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What are some good resources for setting up and debugging neural networks?

I am aware of Troubleshooting Deep Neural Networks by Josh Tobin and A Recipe for Training Neural Networks by Andrej Karpathy, but I am interested in other resources that can give me some guidelines ...
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  • 131
8 votes
1 answer
309 views

Who invented the concept of over-fitting?

I list the references that I found so far. Shortly, the first appearance of the term was in 1670, first appearance in in close meaning was in 1827, first appearance in a biological paper was in 1923 ...
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1 vote
0 answers
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Machine learning for circular sequences

My data are sequences of real numbers $a_0,a_1,...,a_{n-1}$. The length of a sequence is fixed and equals $n$. Each sequence is mapped to a real number $y$ and I want to predict $y$ given the sequence....
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2 votes
0 answers
97 views

What is the difference between ICR and OCR?

I've just found the term "Intelligent Character Recognition" (ICR) on Wikipedia and other pages. According to Wikipedia: In computer science, intelligent character recognition (ICR) is an ...
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1 answer
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Where to upload large (0.5Gb) weights anonymously?

I need to upload a number of checkpoints for ConvNets (weights + optimizers, all dicts of pytorch tensors), each about 0.5Gb anonymously. I don't want to use Google Drive. I trained models on the ...
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3 votes
1 answer
80 views

Outlier/Anomaly Detection History

I have been reading about different methods of anomaly detection, their structure and the way they work. Recently I have been trying to find some scholar articles, writings or books where I can learn ...
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1 vote
1 answer
11k views

What is "lag" in time series forecasting?

I'm studying machine learning (e.i. time series analysis). I encountered an Azure tutorial, Retail Forecasting. https://gallery.azure.ai/Experiment/Retail-Forecasting-Step-2-of-6-train-time-series-...
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2 votes
0 answers
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What could be proper terms for a research direction in natural language processing to measure meaningfulness?

For some time, I did assessments to design metrics on how to recognize well-written and meaningful software requirements. Then I decided to work with Stack Overflow question posts because they are a ...
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2 votes
1 answer
2k views

Tutorial for restricted Boltzmann machine using PyTorch or Tensorflow?

I am trying to find a tutorial on training Restricted Boltzmann machines on some dataset (e.g. MNIST), using either PyTorch or Tensorflow. The few I found are outdated. Can you recommend any?
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10 votes
4 answers
1k views

Math PhD (Nonlinear Programming) switching to Data Science?

I am a math Ph.D. student who is interested in going to the industry as a Data Scientist after graduation. I will briefly give some background on my education before posing my question, so that it is ...
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0 votes
1 answer
377 views

How CNN applies backpropagation to update its weights and biases?

I understand that the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer. However, I do not understand how CNN updates its weights and biases using backpropagation. I ...
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1 vote
1 answer
36 views

Book about distances for data science (can't remember the name)

I saw a book somewhere that listed a big amount of mathematical distance functions (the usual euclidean norms, the discrete distance, the hamming distance, etc) used for data science. The name of the ...
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1 vote
1 answer
45 views

How has data generation evolved over the years (and centuries)?

I would like to know how data generation has evolved through the years. My end goal would be to generate a logarithmic line plot showing the ridiculous increase in data generation. Please, does ...
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4 votes
2 answers
811 views

Given a machine learning algorithm, what is the minimum size of the training set for it?

I understand that the more data we have, the more reliable is our model trained on that data. I also understand that the more parameters a machine learning model has, the more training data it ...
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1 vote
0 answers
19 views

Correlation between (average daily) impressions and user response ratio in social media

I'm doing a preliminary study on data from a (niche) social media platform. Studying the correlations between impressions of an object (aka views), interactions (aka likes, comments, ...) and their ...
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4 votes
1 answer
1k views

What is the best practice to test a ETL pipeline?

In traditional software development practice, before going into production, a piece of code should go through various stages of testing (unit test, integration test, user acceptance test) to secure ...
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0 votes
1 answer
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Protein interaction prediction- how to input this data structure

I know the basics of machine learning and have quite an experience with time series data or data fed in a tabular format. But in the picture, the data is arranged as a graph. Is there a way to input ...
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1 vote
0 answers
84 views

Recognition of objects in almost plain background

I want to recognize climbing holds in a climbing wall. I thought about implementing my own heuristic algorithm for background detection because it's almost plain, but I was wondering if this kind of ...
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2 votes
1 answer
324 views

Do 3D bar charts have advantages over 2D bar charts?

I vaguely remember that there was a study / blog post which made a strong point against 3D bar charts. Do you have a source at hand which compares the two - 2D bar charts and 3D bar charts?
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0 votes
1 answer
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algorithm to auto-download articles from the internet

I have an issued in my homeworks and I thinked if there is an rxisted algorithm or if can i create new one that takes key words like "germany" and "polution" and parses in google scholar. It parses ...
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4 votes
2 answers
141 views

Why Gradient methods work in finding the parameters in Neural Networks?

After reading quite a lot of papers (20-30 or so), I feel that I am quite not understanding things. Let us focus on the supervised learnings (for example). Given a set of data $\mathcal{D}_{train}=\{...
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1 vote
0 answers
22 views

Learning Attention Based Models [books]

Today I read an interesting post to drop RNNs for sequential models. However, the post, unfortunately, didn't go into much detail on how one would study the attention based models and start ...
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7 votes
5 answers
1k views

Mastering NLP: Reading List

I've searched the web and there are hundreds of recommendations on what to read. The time moves on and new better quality techniques are published, so I would like to know what is relevant in 2018? ...
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0 votes
3 answers
572 views

Forecast vs Prediction: What is the difference?

I use the two terms as follows: A prediction model gets features (which can be a time series) as input and gives a fixed-length output (might be multiple values, but "atomic" in some sense) Examples:...
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4 votes
1 answer
137 views

Common Techniques to Generate from a Regression Neural Network Model

I am used to train neural networks that are designed for generation, such as GANs or VAEs. I am wondering what are the common techniques to generate data that would minimize the target/energy learned ...
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1 vote
1 answer
25 views

Voting patterns similarities

I'm interested in any research materials on voting patterns. I have a data set of how PMs (members of parliament) voted in my country during last couple of years. Each PM has 3 buttons: Yes, No, ...
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2 votes
2 answers
630 views

Help me choose a Data Science book in Python [closed]

I've been a Data Scientist for a few years now, but I've only recently started to do most of my work in Python (boy, do I miss ggplot2! But ...
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  • 399
7 votes
3 answers
5k views

Is there any proven disadvantage of transfer learning for CNNs?

Suppose I know that I want to use a ResNet-101 architecture for my specific problem. There are ReseNet-101 models trained on ImageNet. Is there any disadvantage of using those pre-trained models and ...
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  • 17.5k
1 vote
2 answers
176 views

What is the relationship between AI and data science?

I think they share a lot (e.g. machine learning is a subset of both, right?), but maybe both have elements the other doesn't have? Could you name some in that case? Or is one a subset of the other? ...
1 vote
0 answers
142 views

ElasticSearch for data scientists [closed]

This is to seek career advice for a data scientist. What pertains within the role of data scientist and what does not regarding ElasticSearch. Does backend development for ElasticSearch using ...
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17 votes
5 answers
4k views

Beginner math books for Machine Learning

I'm a Computer Science engineer with no background in statistics or advanced math. I'm studying the book Python Machine Learning by Raschka and Mirjalili, but when I tried to understand the math of ...
1 vote
0 answers
24 views

Possibility of self-growing neural nets

What is the state of the art for DNNs to infer that they need more neurons than in the initial setup? Can they be acquired dynamically from the underlying "resource substrate"?
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1 vote
1 answer
31 views

Classes of neural nets and their applications

Would you say, you could design, tune and/or train any DNN for any application, or do their designs inherently postulate some specialization? Is there such a review? For example, are CNNs better for ...
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-2 votes
1 answer
53 views

Can you use machine learning to extract the entropy from a hand print reliably?

Let's say that I want to generate a cryptographic key based on my hand (I'm using hands just as an example). What I could do is I scan my hand (so that the friction ridges are apparent) and then use a ...
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2 votes
2 answers
59 views

Deep Learning to estimate what is beyond the edge

I have an image of some data which is approximately 4,000 x 8,000 pixels. I am interested in finding out if anyone has used a deep learning algorithm to predict what would be on the image if it ...
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  • 255
1 vote
1 answer
188 views

MOOCs for Python in Data Science

Thanks also to SE, I've recently changed job and now I'm working in Data Science, mainly on Analytics for the IoT (Internet of Things). Analytics are applications on cloud platforms which collect real-...
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4 votes
2 answers
1k views

MOOC or book on Deep Learning in Python for someone with a basic knowledge of neural networks

I know the concept of a neural network, and I followed the Machine Learning course by Andrew Ng on Coursera, so I also coded some simple ones. However, I miss all the new tricks which are useful to ...
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  • 399
8 votes
1 answer
10k views

Keras categorical_crossentropy loss (and accuracy)

When training a neural network with keras for the categorical_crossentropy loss, how exactly is the loss defined? I expect it to be the average over all samples of $$\textstyle\text{loss}(p^\text{true}...
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  • 194
2 votes
1 answer
407 views

Data Scientist Consulting Interview Guide [closed]

Does anyone have any books or blogs that specifically sheds light on questions to ask your organization (from a consulting POV) as a data scientist? I am a new data scientist, which I have a ...
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4 votes
1 answer
2k views

Has anyone tried to use the hierarchy of ImageNet?

The classes of ImagNet have a hierarchy. Did anybody try to build a hierarchy of classifiers to use this fact? Searching for "multistage classification" leads to different results.
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8 votes
4 answers
6k views

Which book is a standard for introduction to genetic algorithms?

I have heard of genetic algorithms, but I have never seen practical examples and I've never got a systematic introduction to them. I am now looking for a textbook which introduces genetic algorithms ...
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0 votes
1 answer
387 views

OpenNLP tutorial or book

I am very new to Java and primarily a Python and R user. I want to use OpenNLP for certain NLP tasks that I have been entrusted with. Is there any good tutorial or book on OpenNLP that anyone can ...
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18 votes
4 answers
5k views

Data science / machine learning books for mathematicians

I have found other requests for references here. In particular in: Where to start, which books and Books about the "Science" in Data Science? I have given a glance to: Artificial ...
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