Questions tagged [deep-learning]
a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.
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ML approach for quantifying building quality perception
I'm working on a project to model public perceptions of buildings in a tourism context, focusing on attributes like beauty and mystery. The data I have is a labeled dataset of building photos, each ...
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Lack of Variability in Predictions from Multivariate LSTM Model
I've been working on a multivariate LSTM model for time series forecasting, but I'm encountering an issue where the predicted output doesn't exhibit enough variability. The predictions tend to be too ...
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Looking for datasets on automobile parts information for machine learning
I'm embarking on a machine learning project that requires a comprehensive dataset of automobile parts information. The goal is to train a model that can identify and categorize various auto parts, ...
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Car Make and Model detection
I am trying to develop a deep learning model that given an image of a car, it detects a car's make and model among 50 different brands, each with say another 50 models. What approach is probably the ...
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How to get / calculate statistics about voxceleb2?
VoxCeleb2 contains ~1M utterances from ~6000 speakers (multilingual).
I want to get statistics about voxceleb2:
number of utterances & speakers for each language
I tried to find the metadata ...
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How to selectively train a deep model based on the unavailability of a subset of the feature set
I am creating a deep learning binary classification model. Each sample in the dataset contains two mutually exclusive feature sets X and Y.
Feature set X is present in all samples; however, there are ...
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Prefix tuning in LLM uses learnable vectors to fine tune the model
I would like to implement a new architecture for Transformer.
Below description is my thought.
Prefix tuning in LLM uses learnable vectors to fine tune the model.
Is there a way to use the output ...
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Saving and Loading PyTorch Models for Inference without Model Definition
I'm working on a PyTorch project where I need to save and then later load a model for inference in an environment where the model definition is not available. Essentially, I want to load the model (...
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Why the test accuracy showing some odd behaviour in comparison to train accuracy?
I am currently training an ANN using Sequential(a class from Keras API within tensorflow), and I am optimizing the model's architecture and came across something I have not seen before.
The graph of ...
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Frameworks vs models
My second question (Models vs algorithms) is what is the difference between models and frameworks?
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How to create a bot for a real-time PvP game using machine learning?
Unfortunately, I am not well-versed in machine learning. However, I'm trying to understand if it's possible to create a bot for a real-time PvP game like, for example, Clash Royale or Random Dice: ...
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Combining Computer Vision and traditional machine learning to predict respondent reactions (from a survey) to seeing a picture. Is it possible?
I have the following task Computer Vision/prediction task which I’m interested in hearing whether you guys think is feasible.
I have a dataset of 1000 respondents coming from a survey, where ...
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Performing Multi label text classification
I have a text and it's class, so I have performed single text classification, but now I want to train a multi label classifier, so I tried combining sentence to form a multi label dataset, but the ...
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how do I label my images for computer vision
So I want to do shape recognition task on a flowchart using CNN, but my input images are not labeled and I don't know how to do that automaticaly I mean not manually, anyone can help me please ?
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Using cropped background images as background class
I’m currently working on a binary image classification problem using high resolution (up to 6000x4000 pixels) images with complex backgrounds, and CNN transfer learning.
In order to reduce Images size ...
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Feedforward Deep neural network
Is there anyone capable of converting this diagram? Please see the image description provided.
Similar to this
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Feedforward Deep neural networks
Hello everyone can you help me to create a diagram for these F-DNN
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activation=tf.keras.activations.relu vs activation='relu'
Both models are for binary classification problems
Model 1
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Confusion with tensorflow's Sequential Dense Layers
I'm working on a regression probem using Tensorflow, and have created two models with slight differences in their first Dense layer.
The Models
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How high of a correlation coefficient of a feature with a target variable is considered too high?
Currently my classification model is doing too well on all of the train, validation, and test datasets. I'm assuming there is a data leakage in the features, and therefore I've computed the ...
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Measuring Product Search effectiveness
I want to measure the effectiveness of my search engine, one of the ways i can do that is by measuring the rate at which a customer reformulates the previous query. Hence, I need to quantify inter-...
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How was the word2vec model trained?
Let's take the CBOW (continuous bag of words) model as the example.
Suppose that, there are $c$ context words, each of which is a one-hot encoding vector. So the total number of elements of input ...
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Can't overfit Transformer Encoder
In the below code I am trying to train a very simple Transformer Encoder model to basically do nothing with its input. Giving some arbitrary input vector x, the aim of the model is then to output that ...
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Neural Network for binary classification not working
I have made a neural network that was working correctly as a multi-class classifier, but after changing the loss and the activation function, plus the output layer to just 1 neuron, it is not working ...
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Do ML model measurements and validation standards (e.g. NIST, ISO) exists for the finance, healthcare, and technology industries? Provide citations
Normally, for example, we talk about splitting datasets into training and test datasets. But. The splitting % per train and test sets happens in a subjective manner. Sometimes. The train is 60% or 70%,...
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Competition test set performance much lower than validation set
We are a team of 3 participating in a university competition for a deep learning course. The competition involves a binary image classification task where we have to predict leaf diseases on a (5200, ...
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Should non-trainable functions be part of a nn model?
Some explanation for the somewhat obscure title:
I want to train a model which can produce images given some input data. However, actually I want the model to learn some abstract representation which ...
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What do we mean by optimizer.zero_grad()
This should be a simple question. But it is vague to me. What do we mean by optimizer.zero_grad(). Consider SGD as an example: $W^{t+1}= W^{t}- \lambda g_t$. Which one becomes zero for each batch. It ...
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Test accuracy is very low, compare to Trian and validation accuracy for image classification for 400 class
I am working on image classification with 400 class , during training , I am getting good training and validation accuracy , but test accuracy is approximate 0-1% .My input image is 1 scale , with ...
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Adding multi-image context to a CNN
I'm looking for an approach to classify a similar dataset to the exposed next. Let's say we have an image with some elements inside it (imagine a large building footprint with several structures). ...
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How can I approach this transactions data problem?
I am trying to approach the following problem: Imagine that I am a bank and I have a dataframe of transactions that customers make, the columns that this dataframe has are transaction date, customer ...
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changing to gray scale
I want to transfer X-ray data images to grayscale in this code
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Why are low probabilities problematic for knowledge destilation?
Recently, I have been reading the Knowledge Distillation paper (Distilling the Knowledge in a Neural Network) and I have two main questions:
Neural networks typically produce class probabilities by ...
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PyTorch ResNet implementation's Training Loss increasing with every Epochs
I'm implementing a ResNet network from scratch using PyTorch. This network is unique to my requirements, since I need to perform Image Classification for Satellite Imagery with 14 different channels ...
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Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?
Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs?
This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
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How to choose the correct NN model if the metrics are different in training and test time?
I am trying to build an LSTM model which has a lot of Dropout and Batch Norm Layers. When I run model.fit, the accuracy comes out to around 0.7 on the training data....
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Align Vectors are Easy to Learn?
I have three vectors $x,y_1,y_2\in\mathbb{R}^{n\times 1}$, where $x=y_1$, $x\perp y_2$. If I use $x$ as input of a 2-layer perceptron, will regressing $y_1$ be easier than $y_2$ (i.e., when fully ...
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Doubts on a custom loss function for regression problems
From what I read, I know we don't use log loss or cross entropy for regression problems. However, the entire logic behind binary cross entropy(say) is to firstly squeeze the y_hat between 0 and 1 (...
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【NLP】Is there a model or task that determines contextual similarity?
I am trying to work on an engagement detection task in which I have to determine if a student is engaged in class.
I am looking for an NLP approach where I can calculate the similarity score of a ...
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decision tree limitation VS deep learning
I wonder if decision trees (and their derivatives like Random Forest and Gradient Boosting) have interpolation power as deep learning based model. Most of my experience is with deep learning model.
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How to use additional features in image captioning?
I have the following question - is it possible to train a model based on Transformer architecture to use additional attributes to generate a caption for an image?
For example, I have a dataset with ...
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Deep Q-Learning: How are network parameters updated, and why consider episodes in the first place?
I'm trying to wrap my head around the implementation of deep $Q$-learning, and why we even consider episodes in the first place. The usual set-up is that we initialize some starting state $s_0$, then ...
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Which image classification methods/models could suit my (product) image classification problem?
Say you are a potato chips company. The goal is to have consumers upload images of the product they are having issues with and be able to identify the product by brand/variant using machine learning. ...
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ML paper reproducibility
How can I reproduce results in an ML paper if I don't have the identical resources to train the models as in the paper ? (in my case I only have a laptop spec NVidia gpu and in most of the papers I ...
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Meaning of mean squared error in multistep prediction
In multistep prediction with LSTM(keras), say we had this kind of result:
target = [[1,2,3] ,[4,5,6] ]
predictions = [[1.1,2.2,3.3] , [4.4,5.5,6.6]]
When we choose mean_squared_error as the loss ...
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3D Design file labelling and classification for manufacturing
I have ~1 million 3D design (.STP and/or .OBJ) files of various parts for medical devices, aerospace, automotive or defense systems. I'd like to label them based on appropriate manufacturing methods ...
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Has someone designed a neural network which can select its own activation functions and/or have multiple activation functions in one model?
I'm wonder if there are any papers or implementations where a neural network has multiple activation functions in a single model (and layer), and preferably also where such activation functions ...
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Understanding Multi-headed Attention from architecture details
I've a conceptual question
BERT-base has a dimension of 768 for query, key and value and 12 heads (Hidden dimension=768, number of heads=12). The same is conveyed if we see the BERT-base architecture
<...
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Role of stateful parameter vs shuffle parameter in LSTM keras
I'm trying to make prediction on a multivariate time series using LSTM. I know stateful=True in keras LSTM means state(hidden) of each sequence, in a batch, at index i - is passed to the next batch, ...
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How is it called when instead of creating predective models finding patterns in observed data (ML) you tried to guess the model theorically...?
I'm a college student appasionated of machine learning and I've decided to my bachelor thesis about it. I thought that as an interesting introduction to machine learning, I could introduce it by ...
