Linked Questions

0
votes
1answer
877 views

Is there a rule for number of hidden nodes in a layer and number of hidden layers [duplicate]

Is there a rule of thumb for selecting for a neural network or an autoencoder: (a) Number of hidden neurons (b) Number of hidden layers (c) In general, to begin applying a machine learning ...
2
votes
0answers
57 views

How to choose number of neurons and hidden layers? [duplicate]

I followed this guy's tutorial on YouTube. Following is the code that was used for classifying 0 to 9 handwritten digits from MNIST dataset. The dataset contains 70,000 images of 28 x 28. Here, 60,000 ...
0
votes
0answers
11 views

Design of multi layer Neural network [duplicate]

For glaucoma classification: EG: 1. I have extracted features from the retinal fundus like CDR, NRR, PHOG etc. 2. i have to use multi layer NN for classification purpose only ( normal or affected) ...
17
votes
4answers
7k views

What does “degree of freedom” mean in neural networks?

In Bishop's book "Pattern Classification and Machine Learning", it describes a technique for regularization in the context of neural networks. However, I don't understand a paragraph describing that ...
12
votes
1answer
47k views

Difference between “in-sample” and “pseudo out-of-sample” forecasts

Is there an explicit difference between in-sample forecasts and pseudo out-of-sample forecasts. Both is meant in the context of evaluating and comparing forecasting models.
7
votes
2answers
7k views

Time series prediction: Neural Network (nnetar) vs. exponential smoothing (ets)

When I make a forecast for the univariate time series $x_1=1, x_2=2, \dots, x_{14} = 14$, why does the nnetar() function in R (which uses a neural network) not ...
6
votes
2answers
5k views

ML with fastest classification speed

I have a data classification problem and I'm wondering what is the best machine learning approach to use for the particular constraints of my problem. My constraints are as follows: - the data ...
4
votes
1answer
10k views

Optimum number of epochs and neurons for an LSTM network

I wanted to know if there's a way to select an optimum number of epochs and neurons to forecast a certain time series using LSTM, the motive being automation of the forecasting problem, i.e. the ...
5
votes
1answer
1k views

Is building deep learning architectures a trial and error scheme?

I have been reading many deep learning papers where each of them follow different architecture. I cannot see what the logical sense or the intuitive sense behind each layer in each architecture. I got ...
6
votes
1answer
5k views

How to Choose Activation Functions in a Regression Neural Network?

I'm having difficulties with some basics regarding the application of feed forward neural networks for regression. To be specific, lets say that I have an input variable $x \in \mathbb R^4$ and data ...
2
votes
2answers
3k views

How to verify that the ANN code is working properly?

First I'm not sure if this is the right place to post my question, but I saw some questions about ANN, and I assumed I can ask it here. I have implemented an ANN with back-propagation. I'm using it ...
2
votes
1answer
2k views

Feed forward Neural Network and MSE issues

I've been implementing a Feed-forward Neural Network in C++ and CUDA. It is a basic Multi-layered Feed Forward ANN, using various activation functions (sigmoid bipolar, tanh, tanh scaled, and soft-...
0
votes
3answers
593 views

Less training data gave me better test score

I'm currently working on a project where I'm also the one who's labelling the data that im going to use for the model. My model is a document-classification model where I'd classify if a document ...
8
votes
0answers
933 views

Universal Approximation Theorem — Neural Networks [closed]

I have posted this question elsewhere--MSE-Meta, MSE, TCS, MetaOptimize. Previously, no one had given a solution. But now, here is a really excellent and comprehensive answer. Universal approximation ...
4
votes
1answer
406 views

2 hidden layers are more powerful than 1

When searching for information on choosing the number of hidden layers in a neural network, I have come across the following table mutiple times, including in this answer: | Number of Hidden Layers ...

15 30 50 per page