1k 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 ...
168 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 ...
38 views

### machine learning and neural network - hidden layers [duplicate]

you have been hired by a gem mining company to develop a classification system that can classify gems as part of the automated sorting system. you decided to use a network with one hidden layer. how ...
37 views

### Number of units in fully connected layers [duplicate]

I would like to build a fully-connected network for classifying three classes. As input I have two feature sets. One feature set with 100 features and another feature set with 1000 features. The input ...
31 views

### Rule of thumb for choosing NN parameters in a regression analysis [duplicate]

I am building a simple NN for a regression analysis (0.5 mln rows of data) in Keras. ...
27 views

### Is ther any logic that migth help while you create a Neural Network? [duplicate]

I am a newbie in Tensorflow and Keras and for the first moment I ask me the same few questions while I am creating my Neural Network. Even searching for some explanation of how to estructure, train, ...
20 views

### How to obtain the optimum model using Keras? Deep Learning [duplicate]

There are too many parameters while building an artificial neural network. Some of which that comes to my mind are: Number of layers Types of layers Number of nodes in each level of layer Activation ...
9k 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 ...
50k 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.
10k 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 ...
8k 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 ...
15k 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 ...
2k 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 ...
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 ...