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Questions tagged [deep-learning]

For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".

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69
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8answers
12k views

Do scientists know what is happening inside artificial neural networks?

Do scientists or research experts know from the kitchen what is happening inside complex "deep" neural network with at least millions of connections firing at an instant? Do they understand the ...
29
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3answers
17k views

Why is Lisp such a good language for AI?

I've heard before from computer scientists and from researchers in the area of AI that that Lisp is a good language for research and development in artificial intelligence. Does this still apply, with ...
14
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5answers
2k views

What is the difference between machine learning and deep learning?

Can someone explain to me the difference between machine learning and deep learning? Is it possible to learn deep learning without knowing machine learning?
13
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6answers
6k views

What is the purpose of an activation function in Neural Networks?

It is said that activation functions in neural networks help introduce non-linearity. What does this mean? What does non-linearity mean in this context? How does introduction of this non-linearity ...
9
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2answers
492 views

Is topological sophistication necessary to the furtherance of AI?

The current machine learning trend is interpreted by some new to the disciplines of AI as meaning that MLPs, CNNs, and RNNs can exhibit human intelligence. It is true that these orthogonal structures ...
41
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3answers
27k views

How can neural networks deal with varying input sizes?

As far as I can tell, neural networks have a fixed number of neurons in the input layer. If neural networks are used in a context like NLP, sentences or blocks of text of varying sizes are fed to a ...
28
votes
8answers
24k views

In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?

My understanding is that the convolutional layer of a convolutional neural network has four dimensions: input_channels, filter_height, filter_width, number_of_filters. Furthermore, it is my ...
5
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3answers
728 views

Are neural networks statistical models?

By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models. In contrast Machine Learning is not just glorified Statistics. I am looking ...
21
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4answers
3k 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 ...
9
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1answer
5k views

Policy gradients for multiple continuous actions

Question is regarding Deep Reinforcement Learning using Policy Gradients. Cutting edge policy gradients algorithms are TRPO (Trusted Region Policy Optimization) and PPO (Proximal Policy Optimization)....
10
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2answers
2k views

Which layer consumes more time in CNN training ? Convolution layers vs FC layers

In Convolutional Neural Network, which layer consumes maximum time in training? Convolution layers or Fully Connected layers? We can take AlexNet architecture to understand this. I want to see time ...
6
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2answers
342 views

What is experience replay in laymen's terms?

I've been reading Google's DeepMind Atari paper and I'm trying to understand the concept of "experience replay". Experience replay comes up in a lot of other reinforcement learning papers (...
5
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3answers
3k views

CPU preferences and specifications for a multi GPU deep-learning setup

For a multi (4xTitan Xp) GPU deep learning setup what kind of CPU is preferable? Specifically I am comparing: Intel Xeon E5-2620 with 8x2.1GHz 20MB L3 Cache Intel Xeon E5-1620K with 4x3.5Ghz 10MB L3 ...
2
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2answers
149 views

Concrete Example for Q Learning

I am not sure if I understood the q learning algorithms correctly. Therefore I would give a concrete example and ask if someone can tell me how to update the q value correctly. First I initialized ...
2
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2answers
582 views

Does fp32 & fp64 performance of GPU affect deep learning model training?

I am purchasing Titan RTX GPU. Everything seems fine with that except float32 & float64 performance which seems lower vis-a-vis some of its counter parts. I wanted to understand if single ...
14
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3answers
21k views

How to handle images of large sizes in CNN?

Suppose there are 10K images of sizes 2400 x 2400 are required to use in CNN.Acc to my view conventional computers the people use will be of use. Now the question is how to handle such large image ...
14
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2answers
1k views

What's the main concept behind Capsule Networks? [duplicate]

As you might know, Capsule Networks have been recently introduced by Hinton. There also have been several heads up within his talks. As expected, the paper elaborates on the idea way theoretically! ...
13
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2answers
891 views

When is deep learning overkill?

For example, for classifying emails as spam, is it worthwhile - from a time/accuracy perspective - to apply deep learning (if possible) instead of another machine learning algorithm? Will deep ...
16
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3answers
21k views

Understanding GAN loss function

I'm struggling to understand the GAN loss function as provided in Understanding Generative Adversarial Networks (a blog post written by Daniel Seita). In the standard cross-entropy loss, we have an ...
9
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3answers
9k views

What is the concept of Tensorflow Bottlenecks?

What is the concept and how does one calculate Bottleneck values? How do these values help image classification? Please explain in simple words.
11
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3answers
2k views

How to implement a constrained action space in reinforcement learning?

I'm coding a reinforcement learning model with a PPO agent thanks to the very good Tensorforce library, built on top of Tensorflow. The first version was very simple and I'm now diving into a more ...
10
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3answers
10k views

Measuring Object size using Deep Neural Network

I have a large dataset of vehicles with the ground truth of their lengths (Over 100k samples). Is it possible to train a deep network to measure/estimate vehicle length ? I haven't seen any papers ...
6
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3answers
776 views

Board/Card Game AI - Questions concerning state/action space - Deep Reinforcement Learning

Ok, I now know how a machine can learn to play to play Atari games (Breakout): Playing Atari with Reinforcement Learning With the same technique it is even possible to play FPS games (Doom): Playing ...
4
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3answers
255 views

Can someone direct me to a sites and/or videos that can bring an absolute beginner up to speed with AI?

To start, I'm not a programmer/computer scientist/et al... - I work in Finance and have, through my job, self-thought myself VBA for excel and outlook and would consider myself as being in the upper ...
3
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1answer
56 views

Combining different trained neural networks

I'm relatively new to this whole AI thing and have a question.. Let's say I have two different fully trained neural networks. The first one is trained for mathematical addition and the second one on ...
3
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2answers
295 views

Each training run for DDQN agent takes 2 days, and still ends up with -13 avg score, but OpenAi baseline DQN needs only an hour to converge to +18?

Status: For a few weeks now, I have been working on a Double DQN agent for the PongDeterministic-v4 environment, which you can find here. A single training run ...
1
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2answers
2k views

What is non-Euclidean data?

What is non-Euclidean data? Where does this type of data arises? Apparently, graphs and manifolds are non-Euclidean data. Why exactly is that the case? What is the difference between non-Euclidean and ...
5
votes
3answers
553 views

Use of machine learning for analyzing companies enlisted in stock market

Can current trends and tools, in the field of machine learning, replicate the complexity of financial market? If yes, then what are the tools available in this domain. Q. I am trying to build a model ...
4
votes
1answer
342 views

How would you encode your input vector/matrix from a sequence of moves in game like tasks to train an AI? e.g. Chess AI?

I've seen data sets for classification / regressions tasks in domains such as credit default detection, object identification in an image, stock price prediction etc. All of these data sets could ...
3
votes
1answer
606 views

Dataset containing images of varying dimensions and orientations

I am new to deep learning. I have a dataset of images of varying dimensions of a certain object. A few images of the object are also in varying orientations. The objective is to learn the features ...
2
votes
1answer
48 views

Video engagement analysis with deep learning

I am trying to rank video scenes/frames based on how appealing they are for a viewer. Basically, how "interesting" or "attractive" a scene inside a video can be for a viewer. My final goal is to ...
2
votes
1answer
55 views

Aesthetics analysis with deep learning

I'm trying to score video scenes in terms of aesthetics and cinematography features. Basically, how "interesting" a scene or video frame can be for a viewer. Simpler, how attractive a scene is. My ...
2
votes
1answer
259 views

AI chatbot design

I am looking to train a chatbot that can help me relieve stress and deal with my negative emotions. I would like for the chatbot to be like the ones that pass the Turing test, remain professional, yet ...
1
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0answers
82 views

Novelty Search Mutation Algorithm

I have been reading a lot lately about some very promising work coming out of Uber's AI Labs using mutation algorithms enhanced with NOVELTY SEARCH to evolve deep neural nets. https://www.arxiv-...
0
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1answer
34 views

Use deep learning to rank video scenes

I'm new to machine learning and especially, deep learning. Given a video (and it's subtitle), I need to generate a 10-second summary out of this video. How can I use ML and DL to produce the most ...
6
votes
1answer
2k views

How to write C decompiler using AI?

I would like to learn more whether it is possible and how to write a program which decompiles executable binary (an object file) to the C source. I'm not asking exactly 'how', but rather how this can ...
6
votes
1answer
107 views

Synapses automatically select it's neurons

I know the basics of Artificial Neural Networks. For instance; make dot product with the weights and every neuron from previous layer. Adjust the weight by error. And done, That is how I see neural ...
4
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3answers
655 views

Convolutional neural nets and reduction of the layers

I have a very simple question about Conv nets. I understand the whole principle, but only one thing is not well explained on the Internet. If I have a 16 channels image that goes on a convolutional ...
3
votes
2answers
819 views

Predicting chemical reactions using AI

Is there any research which study application of AI into chemistry which can predict the output of certain chemical reactions. So for example, you train the AI about current compounds, substances, ...
3
votes
1answer
3k views

Identifying cars using deep learning

I would like to use deep leaning for identifying cars; I want the system to predict wether an object is a car or not. How can I do that knowing that im still a beginner in the Deep Learning field ? I ...
3
votes
3answers
303 views

What is the difference between encoders and auto-encoders?

How are the layers in a encoder connected across the network for normal encoders and auto-encoders? In general, what is the difference between encoders and auto-encoders?
2
votes
1answer
85 views

How to find the category of a technical text on a surface-semantic-level

There are some predefined categories( Overview, Data Architecture,Technical Details, Applications etc). The requirement is to classify the input text of paragraphs into their resp. category. I cant ...
2
votes
3answers
159 views

What is chaotic behavior and how it is achieved in non-linear regression and artificial networks?

I'm finding it hard to understand the relationship between chaotic behavior, the human brain, and artificial networks. There are a number of explanations on the web, but it would be very helpful if I ...
2
votes
1answer
53 views

Deep learning model training and processing requirement for Traffic data

I am a newbie in the deep learning and am looking for advice on predicting traffic congestion events. I have a table for vehicles travel times data, another table with the roads length segmented based ...
2
votes
1answer
29 views

How much the dialects recognition and speech recognition are relevant?

In this tutorial, they build a speech recognition model to classify a one-second audio clip as one of ten predefined words. Suppose that we modified this problem as the following: Given an Arabic ...
1
vote
1answer
68 views

Usefulness of Dropout for non-overfitting network

My neural network is simple enough and does not overfit. Dropout is a regularization technique for reducing overfitting in neural networks From Wikipedia Adding Dropout in a non-overfitting ...
1
vote
0answers
92 views

What is a graph neural network?

What is a graph neural network (GNN)? How is a GNN different from a NN? How exactly is a GNN related to graphs? What are the components of a GNN? What are the inputs and outputs of GNNs? How can ...
1
vote
1answer
153 views

Does backpropagation update weights one layer at a time?

I am new to Deep Learning. Suppose that we have a neural network with one input layer, one output layer, and one hidden layer. Let's refer to the weights from input to hidden as $W$ and the weights ...
1
vote
1answer
96 views

Is back propagation applied for each data point or for a batch of data points?

I am new to deep learning and trying to understand the concept of back propagation. I have a doubt on when the back propagation is applied. Assume that I have a training data set of 1000 images for ...
1
vote
1answer
64 views

Interpretation of a good overfitting score

As shown below, my deep neural network is overfitting : where the blue lines is the metrics obtained with training set and red lines with validation set Is there anything I can infer from the fact ...