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1500 questions
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1 answer

Multi-dimentional and multivariate Time-Series forecast (RNN/LSTM) Keras

I have been trying to understand how to represent and shape data to make a multidimentional and multivariate time series forecast using Keras (or TensorFlow) but I am still very unclear after reading many blog posts/tutorials/documentation about how…
Bastien
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What does it mean when we say most of the points in a hypercube are at the boundary?

If I have a 50 dimensional hypercube. And I define it's boundary by $0
Rohit Kumar Singh
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5 answers

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 the Machine Learning, I wasn't able to understand…
Tantaros
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2 answers

Item based and user based recommendation difference in Mahout

I would like to know how exactly mahout user based and item based recommendation differ from each other. It defines that User-based: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of…
Sreejithc321
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2 answers

Strange behavior with Adam optimizer when training for too long

I'm trying to train a single perceptron (1000 input units, 1 output, no hidden layers) on 64 randomly generated data points. I'm using Pytorch using the Adam optimizer: import torch from torch.autograd import Variable torch.manual_seed(545345) N,…
Bai Li
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16
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1 answer

How many features to sample using Random Forests

The Wikipedia page which quotes "The Elements of Statistical Learning" says: Typically, for a classification problem with $p$ features, $\lfloor \sqrt{p}\rfloor$ features are used in each split. I understand that this is a fairly good educated…
Valentin Calomme
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16
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1 answer

RNN using multiple time series

I am trying to create a neural network using time series as input, in order to train it based on the type of each series. I read that using RNNs you can split the input into batches and use every point of the time series into individual neurons and…
Ploo
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2 answers

Where in the workflow should we deal with missing data?

I'm building a workflow for creating machine learning models (in my case, using Python's pandas and sklearn packages) from data pulled from a very large database (here, Vertica by way of SQL and pyodbc), and a critical step in that process involves…
Therriault
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2 answers

Why do we normalize the discounted rewards when doing policy gradient reinforcement learning?

I'm trying to understand the policy gradient approach for solving the cartpole problem. In this approach, we're expressing the gradient of the loss w.r.t each parameter of our policy as an expectation of the sum of gradients of our policy gradient…
ihadanny
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How much data are sufficient to train my machine learning model?

I've been working on machine learning and bioinformatics for a while, and today I had a conversation with a colleague about the main general issues of data mining. My colleague (who is a machine learning expert) said that, in his opinion, the…
DavideChicco.it
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2 answers

How to calculate VC-dimension?

Im studying machine learning, and I would like to know how to calculate VC-dimension. For example: $h(x)=\begin{cases} 1 &\mbox{if } a\leq x \leq b \\ 0 & \mbox{else } \end{cases} $, with parameters $(a,b) ∈ R^2$. What is the VC-dimension of it?
铭声孙
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3 answers

Intuition Behind Restricted Boltzmann Machine (RBM)

I went through Geoff Hinton's Neural Networks course on Coursera and also through introduction to restricted boltzmann machines, still I didn't understand the intuition behind RBMs. Why do we need to compute energy in this machine? And what is the…
Born2Code
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How does Xgboost learn what are the inputs for missing values?

So from Algorithm 3 of https://arxiv.org/pdf/1603.02754v3.pdf, it says that an optimum default direction is determined and the missing values will go in that direction. However, or perhaps I have misunderstood/missed the explanation from the…
mathnoob
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How to define a custom performance metric in Keras?

I tried to define a custom metric fuction (F1-Score) in Keras (Tensorflow backend) according to the following: def f1_score(tags, predicted): tags = set(tags) predicted = set(predicted) tp = len(tags & predicted) fp =…
Hendrik
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Back-propagation through max pooling layers

I have a small sub-question to this question. I understand that when back-propagating through a max pooling layer the gradient is routed back in a way that the neuron in the previous layer which was selected as max gets all the gradient. What I'm…
Majster
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