Linked Questions

2
votes
0answers
75 views

Maximum Layers in "any" Neural Network [duplicate]

I have about 6 months of experience in building and using Neural Networks with no prior/formal training. As I explore this field further, I see a lot of discussions about determining how many layers/...
30
votes
6answers
13k views

Why do convolutional neural networks work?

I have often heard people saying that why convolutional neural networks are still poorly understood. Is it known why convolutional neural networks always end up learning increasingly sophisticated ...
5
votes
3answers
4k views

Neural Network Performs Bad On MNIST

I've been struggling with Neural Networks for a while now. I get the math behind backpropagation. Still as reference I'm using the formulas from here. The Network learns XOR: Prediction After ...
8
votes
1answer
16k views

Why should softmax be used in CNN

In the last layer of CNNs and MLPs it is common to use softmax layer or units with sigmoid activation functions for multi-class ...
4
votes
2answers
2k views

Depth of a Neural network

I was self-teaching myself. I totally understand why depth of a neural network affects the learning and how it differs than its width. But I am looking for some theoretical justification about it. ...
1
vote
3answers
1k views

More layers in NN give worse result

So I was working on a classification task with the help of a NN. The data-set was normalised, weights random between 0-1, and all the activations were sigmoid ...
9
votes
2answers
165 views

Is there any consensus on choosing an appropriate ML approach?

I am studying data science at the moment and we are taught a dizzying variety of basic regression/classification techniques (linear, logistic, trees, splines, ANN, SVM, MARS, and so on....), along ...
2
votes
1answer
2k views

Choosing layer hyper-parameters of a CNN

Context: I'm building a CNN on MATLAB to classify wallpaper groups. I'm using the following network type. CONV -> ReLU -> POOL -> CONV -> ReLU -> POOL -> FC -> ...
5
votes
1answer
360 views

Fully connected layer in deep learning

How to determine the best number of the fully connected layers in CNN? Can I use only one fully connected layer in CNN? How to determine the dimension of the fully ...
2
votes
3answers
332 views

How each layer of a neural net is responsible for one feature

Through my study of neural networks, I came across the idea that each layer of a neural network is responsible for recognizing one feature of the input data. For example, if we build a neural network ...
2
votes
3answers
534 views

Neural Network beginner level tutorial

I am trying to build a simple multi layer perceptron Neural Network in Java, but apparently my calculations are off. I am looking for a beginner-level tutorial which can help me to understand how to ...
0
votes
1answer
415 views

Properly using activation functions of neural network

I'm trying to understand the hidden layers of neural networks. Input layer section covers the steps that I use before passing information to hidden layer where concerns appear. Input Layer: From my ...
1
vote
2answers
809 views

How to obtain with a recurrent neural network the Xor function using keras? [closed]

I'm trying to implement a model of recurrent neural network to solve the XOR problem, but I am not still able to do that. Any hints?
1
vote
1answer
688 views

Predict method of the perceptron algorithm

Can someone explain to me how the predict method of the perceptron algorithm works? ...
2
votes
2answers
386 views

Should the different layers of deep learning models have same size or they should be changed based on a rule

I see a lot of people varying the width of each layer in a deep neural network. ie. Input->50->100->150->Output. I'm curious what, if any, are the advantages of this structure over static layer widths ...

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