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For questions about a neural networks, such as ANNs, CNNs, RNNs, or any other machine learning components that qualifies as a neural networks in that they simulate key complexity handling aspects of biological neural networks in invertebrates.

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A book that cover both theory and practice for an engineer

This might be a boring question. After reading many Quora questions about this matter it seems that books on AI/ML are too deep or too theoretical. They can be books for people who only talk about AI ...
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0answers
22 views

Why do we need Upsampling and Downsampling in Progressive Growing of Gans

I was working recently on Progressive Growing of GANs (aka PGGANs). I have implemented the whole architecture, but the problem that was ticking my mind is that in simple GANs, like DCGAN, PIX2PIX, we ...
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1answer
44 views

Can you teach an AI through sentences?

If you taught an AI to understand sentences through usual neural network techniques. Then could you being to teach it things with sentences such as "ants are small", "the sky is blue". i.e. if you ...
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0answers
19 views

How does the target output of a Single Shot Detector (SSD) look like?

According to the paper SSD: Single Shot MultiBox Detector, for each cell in a feature map k boxes are acquired and for each box we get $c$ class scores and $4$ offsets relative to the original default ...
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2answers
96 views

Is a Multilayer Perceptron a recursive function?

I read somewhere that a Multilayer Perceptron is a recursive function in its forward propagation phase. I am not sure, what is the recursive part? For me, I would see a MLP as a chained function. So, ...
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2answers
45 views

What does it mean for a neuron in a neural network to be activated?

I just stumbled upon the concept of neuron coverage, which is the ratio of activated neurons and total neurons in a neural network. But what does it mean for a neuron to be "activated"? I know what ...
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0answers
41 views

Loss function for Hierarchical Multi-label classification

I am looking to try different loss functions for a hierarchical multi-label classification problem. So far, I have been training different models or submodels like multilayer perceptron ( MLP )branch ...
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3answers
119 views

How do you program fear into a neural network?

If you've been attacked by a spider once chances are you'll never go near a spider again. In a neural network model, having a bad experience with a spider will slightly decrease the probability you ...
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1answer
34 views

Backpropagation With Medium-sized Neural Networks

So, I've been wanting to make my own Neural Network in Python, in order to better understand how it works. I've been following this series of videos as a sort of guide, but it seems the ...
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0answers
26 views

How recurrent neural network work when predict many days?

I use recurrent neural network, RNNs have to get input one value per step and it will show one value output. If I have daily sale demand time series data. I want to predict sale demand for three ...
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1answer
31 views

Using different timesteps for features and target value

I would like to know whether it's wrong; when working with time series data; to use daily prices as features and the price after 3 days as target. Is this correct or should I use the next-day price as ...
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2answers
39 views

Reinforcement Learning Batch Size

I am using a neural network as my function approximator for reinforcement learning. In order to get it to train well I need to choose a good learning rate. Hand picking one is difficult, so I read up ...
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1answer
42 views

Why coupling coefficients (c) in Capsule networks can't by learned by backpropagation?

The paper Dynamic Routing Between Capsules uses the algorithm called "Dynamic Routing Between Capsules" to determine the coupling coefficients between capsules. Why it can't be done by ...
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2answers
97 views

universal function approximation of neural networks

It has been proven by Cybenko in 1989 that neural networks are universal function approximators, but I have a related question that is somewhat different. Assume the neural network's input and output ...
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3answers
131 views

Are AI algorithms capable of self-repair?

Do AI algorithms exist which are capable of healing themselves or regenerating a hurt area when they detect so? For example: In humans if a certain part of brain gets hurt or removed, neighbouring ...
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2answers
258 views

Can deep networks be trained to prove theorems?

Assume we have a large number of proofs in first order predicate calculus. Assume we also have the axioms, corollaries, and theorems in that area of mathematics in that form too. Consider the each ...
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1answer
26 views

Exploration Strategies for Reinforcement Learning w/ Continuous Action Space

I'm building a deep neural network to serve as the policy estimator in an Actor-Critic reinforcement learning algorithm for a continuing (not episodic) case. I'm trying to determine how to explore ...
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2answers
40 views

What kind of problem is “email text extraction”?

I need to retrieve just the text from emails. The emails can be in HTML format, and can contain huge signatures, disclaimer legalese, and broken HTML from dozens of forwards and replies. But, I only ...
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1answer
44 views

What are SVMs (Support Vector Machines)?

What are SVMs (Support Vector Machines)? Are SVMs a kind of a neural network? (meaning it has nodes and weights, etc). What are best used for? Where I can find information about these for... dummies?...
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0answers
22 views

Using a neural network for learning a Motion Graph?

In a recent paper about progress in computer animation a so called motion graph is used to describe the transition between keyframes of facial animation. Easy Generation of Facial Animation Using ...
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2answers
53 views

Image Classification

I am currently working on a project to classify snake types separately using an image of the snake. I need to train a module to classify snake images, but the problem is there are only a small number ...
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2answers
54 views

A neural network to learn the connection between two images

Is it possible to build a neural network that learns the connection between two images? Let's say I have a number of X images that related to Y images. How can I build a neural network that takes an ...
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2answers
39 views

Behaviour of cost

In doing a project using neural networks with an input layer, 4 hidden layers and an output layer ,I used mini batch gradient descent. I noticed that the randomly initialised weights seemed to do a ...
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1answer
21 views

Dealing with “blank” inputs in prediction of a neural network?

Say I'm training a neural net to compute the following function: (color_of_clothing, body_height) -> gender When using this network for prediction, I can ...
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0answers
46 views

What ANN layer widths support the learning of digit recognition?

I have created an ANN in Python (without libs). On beginning, it had been learned in target of solve linear problems like distinguishing between negative and positive numbers, where the layer widths ...
2
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1answer
53 views

Disentangled VAE doesn't reconstruct accurate grids

I am trying to implement the disentangled VAE model according to this link. I want to understand the architecture of this model in order to customize it later. As infrastructure, I have a linux kernel ...
6
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1answer
158 views

Learning algorithms of Neural Networks

Could you please let me know which of the following classification of Neural Network's learning algorithm is correct? The first one classifies it into: supervised, unsupervised and reinforcement ...
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1answer
79 views

What are the advantages of ReLU vs Leaky ReLU and Parametric ReLU (if any)?

I think that the advantage of using Leaky ReLU instead of ReLU is that in this way we cannot have vanishing gradient. Parametric ReLU has the same advantage with the only difference that the slope of ...
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4answers
158 views

Why do we need floats for using neural networks?

Is it possible to make a neural network that uses only integers by scaling input and output of each function to [-INT_MAX, INT_MAX]? Is there any drawbacks?
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0answers
27 views

What is an embedding and to which parameter does it correspond in a neural network?

What is an embedding, how is it determined, what does the parameter “size” of an embedding mean, to which parameter does it correspond in a neural network?
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0answers
51 views

Backpropagation of convolutional neural network - confusion [closed]

I've already seen many articles about this topic and Backpropagation In Convolutional Neural Networks by Jefkine (5 September 2016) seems to be the best. Although, as author said, For the purposes ...
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1answer
36 views

Is 'job title classification' rather a problem of NLP or machine learning?

first of all I want to specify the data available and what needs to be achieved: I have a huge amount of vacancies (in the millions). The information about the job title and the job description of ...
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2answers
73 views

How to model inhibitory synapses in the artificial neuron? [Biological inspiration]

In the brain some synapses are stimulating and some inhibiting. ReLu erases that property to only stimulating once, since in the brain inhibition doesn't mean 0 output, but more precisely - negative ...
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0answers
25 views

What methods are there to detect discrimination in trained models?

I've been researching AI regulation and compliance (see my related question on law.stackexchange), and one of the big take-aways that I had is that the regulations that apply to a human will apply to ...
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0answers
30 views

Multiple sets of input in Neural network (or other form of ML)

I'm currently working on a research project where I try to apply different kinds of Machine Learning on some existing software I wrote a few years ago. This software will scan for people in the room ...
5
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1answer
76 views

Why not teach to a NN not only what is true, but also what is not true?

I'm not a person who studies neural networks, or does anything that is related with that area, but I have seen a couple of seminars, videos (such as 3Blue1Brown's Series), and what I am always told is ...
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1answer
57 views

Backward Pass for LSTMs

TL;DR I am currently trying to understand the mathematics in Ger's paper Long Short-Term Memory in Recurrent Neural Networks. I have found the document clear and readable so far. On pg. 21 of the ...
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2answers
57 views

How to choose an activation function?

The question is very simple but the answer could be not. Iin my personal experience - and I've not so much on NN - I choose the activation function for the output layer depending on the output that ...
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2answers
36 views

Training Multi class classification (One-vs-all) on Neural network

I am very new to Machine learning and following the course offered by Andrew Ng.I am very confused How we train our neural network on Multi class Classification(suppose take K classes).For K classes ...
3
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1answer
63 views

Representing inputs and outputs for a card game neural network

I'm attempting to create an AI for a card game using reinforcement learning. The basics of the game are that you can have (theoretically) up to 35 cards in your hand, you can also have to up to 35 ...
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4answers
143 views

Beyond neural networks?

Are there possible algorithms that have the potential to replace neural nets in the near future? And do we need that? What is the worst thing of using neural networks in terms of efficiency?
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1answer
37 views

Automatic image classification

I'm a complete newbie to NNs, and I need your advice. I have a set of images of symbols, and my goal is to categorize and divide them into groups of symbols that look alike. Without teaching NN ...
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1answer
24 views

Restoration of localized damaged areas (time signals, but guess also applicable to images)

I am starting to study the capabilities of neural networks for the reconstruction/restoration/... of communication signals. I am feeding my neural network with a signal which has some parts which ...
3
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1answer
47 views

Structure of LSTM RNNs

I have some very basic questions here. This is probably because I didn't read the relevant documents closely enough. If I used some terminology incorrectly, please point them out. Thank you! For ...
2
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1answer
38 views

CNN's vs Densely Connected NN's

In image classification we are generally told the main reason of using CNN's is that densely connected NN's cannot handle so many parameters (10 ^ 6 for a 1000 * 1000 image). My question is, is there ...
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1answer
27 views

n * one_hot VS n_hot encoding for modeling input layer for a card game

How should I design my input layer for the following classification problem? Input: 5 cards in a card game; vocabulary is 52 cards Output: some classification using a neural network How should I ...
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2answers
82 views

Wrong usage of 'Pose' in Matrix Capsules with EM?

In traditional computer vision and computer graphics, the pose matrix is a 4x4 matrix of the form ...
2
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1answer
41 views

Number of Neuron in Q-Learning of Chess

So I just read about deep Q-Learning which is using a neural network for optimization instead of Q-table. I saw the example here: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html and he ...
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1answer
41 views

What are the internal concepts incorporated in IBM's Watson platform?

IBM's Watson acts as a template for developing chat-bots with ease (without coding), but what are the methodologies and concepts that have been used to build it?
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2answers
47 views

How to build my own dataset and model for an LSTM neural network

I have a sort of mathematical problem and I'm not sure which model I should choose to make an LSTM neural network. Currently in my country, there is a system in which certain groups of researchers ...