What's the deal with Deno? We talk with a major contributor to find out. Listen now.

Questions tagged [training]

For questions about training networks, rules systems, or other AI system components.

Filter by
Sorted by
Tagged with
12
votes
3answers
9k views

How to train a neural network for a round based board game?

I'm wondering how to train a neural network for a round based board game like, tic-tac-toe, chess, risk or any other round based game. Getting the next move by inference seems to be pretty straight ...
12
votes
1answer
18k views

Design AI for log file analysis

I'm developing an AI tool to find known equipments' errors and find new patterns of failure. This log file is time based and has known messages (information and error).I'm using a JavaScript library ...
11
votes
3answers
1k views

Why initial weights in neural network are randomized?

This might sound silly to someone who has plenty of experience with neural networks but it bothers me... I mean randomizing initial weights might give you better results that would be somewhat closer ...
11
votes
2answers
4k views

Which layer in a CNN consumes more training time: convolution layers or fully connected layers?

In a convolutional neural network, which layer consumes more training time: convolution layers or fully connected layers? We can take AlexNet architecture to understand this. I want to see the time ...
10
votes
3answers
342 views

Can some one help me understand this paragraph from Nvidia's progressive gan paper?

Furthermore, we observe that mode collapses traditionally plaguing GANs tend to happen very quickly, over the course of a dozen minibatches. Commonly they start when the discriminator overshoots, ...
9
votes
1answer
681 views

What are the best known gradient-free training methods for deep learning?

As I know, the current state of the art methods for training deep learning networks are variants of gradient descent or stochastic gradient descent. What are the best known gradient-free training ...
9
votes
1answer
162 views

Will quantum computing have any kind of effect on the development of AI? [duplicate]

Recently, according to some reports Google achieved something called 'Quantum Supremacy'. Whether its true or not remains to be seen. But my question is does Quantum Computers or the principle they ...
8
votes
2answers
218 views

What is the name of a human-inspired machine learning approach?

I once came across a neural network being trained without back-propagation or genetic algorithms (or using any kind of data sets). It was based on how the human brain learns and adjusts its ...
8
votes
1answer
597 views

Why does 'loss' change depending on the number of epochs chosen?

I am using Keras to train different NN. I would like to know why if I increment the epochs in 1, the result until the new epoch is not the same. I am using shuffle=False, and np.random.seed(2017), and ...
7
votes
5answers
1k views

How can action recognition be achieved?

For example, I would like to train my neural network to recognize the type of actions (e.g. in commercial movies or some real-life videos), so I can "ask" my network in which video or movie (and at ...
6
votes
2answers
165 views

Shortening the development time of a neural network

I am developing an LSTM for sequence tagging. During the development, I do various changes in the system, for example, add new features, change the number of nodes in the hidden layers, etc. After ...
6
votes
1answer
2k views

What are the state space and the state transition function in AI?

I'm studying for my AI final exam, and I'm stuck in the state space representation. I understand initial and goal states, but what I don't understand is the state space and state transition function. ...
6
votes
2answers
12k views

How do I choose the optimal batch size?

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options: batch mode: where the batch size is ...
6
votes
3answers
3k views

Why L1/L2 regularization technique did not improve my accuracy?

I am training a Multilayer Neural Nets with 146 samples (97 for training set, 20 for validation set and 29 for testing set). I am using: automatic differentiation, SGD method, fixed learning rate + ...
6
votes
1answer
170 views

Q-Learning the generic maze solution

After doing some exercices on Q-learning for maze solving, I wondered : my q-learning algorithms solve only ONE maze. The AI doesn't learn how to solve mazes, so how can I achieve it ? For instance ...
6
votes
3answers
334 views

AI composing music

Do you know what AI model would be best for let it learn composing music? I really don't know where to start there. Are there some good papers out there? I would say, if I use a NN, my only option ...
6
votes
2answers
139 views

Evolving network in game

So I wrote simple feed forward neural network that plays tic-tac-toe: 9 neurons in input layers: 1 - my sign, -1 - opponent's sign, 0 - empty; 9 neurons in hidden layer: value calculated using Relu; ...
6
votes
1answer
132 views

How do I predict if it is rainy or not?

I'm building a weather station, where I'm sensing temperature, humidity, air pressure, brightness, $CO_2$, but I don't have a raindrop sensor. Is it possible to create an AI which can say if it's ...
6
votes
3answers
1k views

Does the model learn from the average of all the data points in the mini-batch?

I used the example at - https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_DataManagement/tensorflow_dataset_api.py - to create my own classification model. I used different ...
6
votes
2answers
84 views

Why do very deep non resnet architectures perform worse compared to shallower ones for the same iteration? Shouldn't they just train slower?

My understanding of the vanishing gradient problem in deep networks is that as backprop progresses through the layers the gradients become small, and thus training progresses slower. I'm having a hard ...
6
votes
3answers
152 views

Why do neural networks trained on identical datasets and with identical hyper-parameters have different performances? [closed]

I found that fully connected neural networks trained on identical data sets with identical hyper-parameters can have different performances or accuracies (7-8% of deviation). Is this an unusual ...
5
votes
5answers
2k views

How does an activation function's derivative measure error rate in a neural network?

A blog post called "Text Classification using Neural Networks" states that the derivative of the output of a sigmoid function is used to measure error rates. What is the rationale for this? I ...
5
votes
3answers
59 views

What should we do when we have equal observations with different labels?

Suppose we have a labeled data set with columns $A$, $B$, and $C$ and a binary outcome variable $X$. Suppose we have rows as follows: ...
5
votes
1answer
610 views

What happens to the training data after your machine learning model has been trained?

I am completely new to all this, for the life of me I can't find the answer to this question anywhere on Google. What happens after you have used machine learning to train your model? What happens to ...
5
votes
1answer
94 views

Why evolutionary training of neural networks is not popular?

Evolutionary algorithms are mentioned in some sources as possible to be used to train a neural network (finding weights, not hyperparameters), however I have not heard about one practical application ...
5
votes
1answer
161 views

Is it feasible to train a Machine Learning Model (with image inputs) in an average personal computer?

There are lots of examples of machine learning systems that can recognize objects and extract other information from images with very high precision. To train the models of such systems is necessary (...
5
votes
1answer
188 views

For each epoch, can I use only on a subset of the full training dataset to train the neural network?

If one has a dataset large enough to learn a highly complex function, say learning chess game-play, and the processing time to run mini-batch gradient descent on this entire dataset is too high, can I ...
5
votes
0answers
75 views

How do neural network topologies affect GPU/TPU acceleration?

I was thinking about different neural network topologies for some applications. However, I am not sure how this would affect the efficiency of hardware acceleration using GPU/TPU/some other chip. If, ...
4
votes
2answers
2k views

What is the “thing” which is trained in AI model training

I am a newbie in the fantastic AI world, I have started my learning recently. After a while, my understanding is, we need to feed in tremendous data to train a or many models. Once the training is ...
4
votes
2answers
329 views

Iteratively and adaptively increasing the network size during training

For an experiment that I'm working on, I want to train a deep network in a special way. I want to initialize and train a small network first, then, in a specific way, I want to increase network depth ...
4
votes
3answers
582 views

Why should weights of Neural Networks be initialized to random numbers?

Premise Ok, I know that this question was asked before on ai.SE, on stats.SE and also on SO. So I did my homework in checking before posting my question, but none of them has an answer that fully ...
4
votes
3answers
165 views

Would this relatively small dataset be enough to train a CNN?

Scenario: I am trying to create a dataset with images of choice for different animal classes. I am going to train those images for classification using CNN. Problem: Let's assume I somehow don't have ...
4
votes
1answer
865 views

What is the purpose of the batch size in neural networks?

Why is a batch size needed to update the weights of a neural network? According to that Youtube Video from 3B1B, the weights are updated by calculating the error between expectation and outcome of ...
4
votes
2answers
2k views

Use Machine Learning/Artificial Intelligence to predict next number (n+1) in a given sequence of random increasing integers

The AI must predict the next number in a given sequence of incremental integers (with no obvious pattern) using Python but so far I don't get the intended result! I tried changing the learning rate ...
4
votes
3answers
202 views

What does end-to-end training mean?

In simple words, what does end-to-end training mean, in the context of deep learning?
4
votes
2answers
381 views

Wouldn't convolutional neural network models work better without flattening the input in any stages?

The above model is what really helped me understand the implementation of convolutional neural networks, so based on that, I've got a tricky hypothesis that I want to find more about, since actually ...
4
votes
1answer
140 views

What approach should I use to detect faces in video game footage?

I have set myself the challenge of detecting the locations of players/bots in videos of a well known first person shooter game (this is for a youtube series I'm planning on doing). I'm not sure which ...
4
votes
2answers
163 views

As a starter: what is the form of training data for image processing

What we are doing in the image processing training. We are storing some form of data which is going to act as the knowledge or experience of the system. In which form can the system store it's ...
4
votes
1answer
134 views

How are LSTM's trained for text generation?

I've seen some articles about text generation using LSTMs (or GRUs) for text generation. Basically it seems you train them by folding them out, and putting a letter in each input. But say you trained ...
4
votes
1answer
67 views

What are state-of-the-art ways of using greedy heuristics to initially set the weights of a Deep Q-Network in Reinforcement Learning?

I am interested in the current state-of-the-art ways to use quick, greedy heuristics in order to speed up the learning in a Deep Q-Network in Reinforcement Learning. In classical RL, I initially set ...
4
votes
1answer
188 views

What can be done to correct for sampling bias introduced from (noisy) training data while training a DNN?

The obvious solution is to ensure that the training data is balanced - but in my particular case that is impossible. What corrections can one perform in such a scenario? I know that my training data ...
4
votes
1answer
48 views

How to compare the training performance of a model on different data input?

So I have a deep learning model and three data sets (images). My theory is that one of these data sets should function better when it comes to training a deep learning model (meaning that the model ...
4
votes
2answers
74 views

How long has it taken for autonomous driving cars to be being sold and used on the roads today?

I remember the first time hearing about google trying to make driverless cars. That was YEARS ago! These days, I'm beginning to learn about Neural Nets and other types of ML and I was wondering: ...
4
votes
3answers
486 views

What is the most time-consuming part of training deep networks?

Deep networks notoriously take a long time to train. What is the most time-consuming aspect of training them? Is it the matrix multiplications? Is it the forward pass? Is it some component of the ...
4
votes
1answer
451 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 ...
4
votes
0answers
79 views

Forcing a neural network to be close to a previous model - Regularization through given model

I'm wondering, has anyone seen any paper where one trains a network but biases it to produce similar outputs to a given model (such as one given from expert opinion or it being a previously trained ...
4
votes
0answers
22 views

How do weights changes handles during back-propagation when there are unknown labels

I have a question about how weights are updated during back-propagation for some of my samples that have unknown labels (please note, unknown, not missing). The reason they are unknown is because this ...
4
votes
0answers
53 views

Training and inference for highly-context-sensitive information

What is the best way to train / do inference when the context matters highly as to what the inferred result should be? For example in the image below all people are standing upright, but because of ...
4
votes
1answer
67 views

REINFORCE algorithm for portfolio optimization - problem while training

I'm trying to implement the Reinforce algorithm (Monte Carlo policy gradient) in order to optimize a portfolio of 94 stocks on a daily basis (I have suitable historical data to achieve this). The idea ...
3
votes
2answers
71 views

Is there a way to define the boundaries of the optimal size of a training set?

At a related question in Computer Science SE, a user told: Neural networks typically require a large training set. Is there a way to define the boundaries of the "optimal" size of a training set ...

1
2 3 4 5 6