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H: Dealing with missing data in several features at once
Good day,
What are the approaches for handling missing data in several features (categorical and continuous) at once? I look through each feature and plotted several histograms of the distribution of them, and I think that a simple substitution of value by some ... |
H: Repeated features in Neural Networks with tabular data
When using algorithms like linear regression or least-squares methods, having repeated or highly correlated features can be harmful for the model. For tree based models, they are generally not too strongly affected by highly correlated features. There are no nu... |
H: How does skewed data affect deep neural networks?
I'm playing around with deep neural networks for a regression problem. The dataset I have is skewed right and for a linear regression model, I would typically perform a log transform. Should I be applying the same practice to a DNN?
Specifically, I'm curious how ske... |
H: Macro and micro average for imbalanced binary classes
Micro and macro averaging are metrics for multi-class classification. However, for binary classification when data are imbalanced, it seems that micro and macro precision have different results. My question is that: does it make sense to use micro and macro prec... |
H: Is sampling a valid way to reduce complexity?
I'm facing an issue where I have a massive amount of data that I need to cluster. As we know, clustering algorithms can have a very high O complexity, and I'm looking for ways to reduce the time my algorithm is running.
I want to try a few different approaches, like pre... |
H: Is Cross Validation needed for regression if you already know the predictors in your model?
Let's say you want to model the behavior of Y = X1 + X2 and you know that this is the model you want to make. Whether or not that approximates the true relationship well is unknown. But since you want to be able to have coe... |
H: Query relating to Pandas Rows manipulation
I have a query regarding Pandas data manipulation.
Let's say I have a dataframe, df with following structure.
A B C
1 1 7
5 3 3
3 3 2
7 5 2
5 NaN 2
We have 3 columns in the dataframe A, B & C.
B column consists of mean values wrt A.
For example,
Value of B in 3rd row (whi... |
H: How to Include Features that Apply to Specific Classes
I'm predicting hours that will be worked for building tasks. Due to the overall low sample size, I've stacked multiple related tasks together into a single model. (There may be 100 total samples in a single model, each task having 10 to 20 samples individually)... |
H: How to find correlated knowledge among different documents?
Say I have a sequence of documents clicked by a user, how can I mine the identical or semanticly similar word/knowledge/phrases shared among different documents?
Maybe someone can give a paper or subject relating to my goal?
AI: The simplest approach would... |
H: How to conclude the generality of any classification methods?
Suppose a classification task A, and there exist a lot of methods $M_1, M_2, M_3$.
The task $A$ is measured by a consistent measure.
For instance, the task A can be a binary classification. In this case, F-score, ROC curve can be used.
I did a survey on ... |
H: Calculation of VC dimension of simple neural network
Suppose I have a perceptron with one-hidden layer, with the input - one real number $x \in \mathbb{R}$, and the activation function of the output layers - threshold functions:
$$
\theta(x) =
\begin{cases}
0, x \leq 0 \\
1, x > 0
\end{cases}
$$
The hidden layer m... |
H: LSTM followed by Dense Layer in Keras
I am working on LSTMs and LSTM AutoEncoders, trying different types of architectures for multivariate time series data, using Keras.
Since it is not really practical to use relu in LSTM because of exploding gradients, I added a Dense layer following LSTM, so it is like:
model =... |
H: What is considered short and long text in NLP (document similarity)
What is considered short and long text in NLP?
I'm working on a dataset that contains documents from 10 to 600 words and I'm asking myself if I should treat them differently. Also, I haven't found a source which explicitly defines short and long te... |
H: Neural networks with not-fixed dimension for input and output
I would like to know if it exists a model/method which can deal with input and output of different dimension.
For example, let us say that the maximum number of info we could have is 6 features and 5 output.
Then I could have examples with 4 features and... |
H: np.unique() explanation?
What happends in this numpy function:
https://numpy.org/doc/stable/reference/generated/numpy.unique.html
a = np.array([1, 2, 5, 3, 2])
u, indices = np.unique(a, return_inverse=True)
The results are:
u
array([1, 2, 3, 5)]
indices
array([0,1,3,2,1), dtype=int64)
u[indices]
array([1, 2, 5,... |
H: Augmentation on test dataset and validation dataset
I'm training a segmentation model (computer-vision).
Thus, my dataset contains images and masks (binary segmentation of objects).
I'm augmenting the training dataset (applying random crop, rotation or shift etc.) to get a larger dataset. I don't apply augmentation... |
H: Coloring clusters so that nearby clusters have different colors
I have clustered a large number of points (~3000) into (~400) clusters. I want to plot the data and visualize the clusters. I want to make sure that nearby clusters have different colors. Can anyone recommend an approach to coloring the clusters?
Th... |
H: no decrease loss and val_loss
I try to train a neural network for time series. I use some data from Covid, mainly the goal is knowing 14 days of number of people at hospital to predict the number at J+1.
I have use some early stopping to not over fit, but almost one time over two the learning stop at patience+1 and... |
H: How to increase sales and revenue of a Client?
I was asked this in an interview for a Data Scientist position:
Lets say Holland and Barret came to you and said they'd like to increase their sales and revenue. How will you go about it?
My answer wasn't hitting the mark or touching the points the interviewer was look... |
H: Which learning rate should I choose?
I'm training a segmentation model, Unet++, on 2d images and I am now trying to find the optimal learning rate.
The backbone of the model is Resnet34, I use Adam optimizer and the loss function is the dice loss function.
Also, I use a few callbacksfunctions:
callbacks = [
... |
H: multi regression for energy data
I'm trying to develop a multi regression model to predict energy consumption during one day period.
X-set dimension is (10178, 52) and consist of 52-feature and Y-set dimension is (10178, 48) as output. I have used the following code:
xtrain, xtest, ytrain, ytest=train_test_split(X,... |
H: What is the difference between active learning and reinforcement learning?
From Wikipedia:
Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs.
Reinforcement learnin... |
H: Math behind 2D convolution for RGB images
I read many threads discussing why 2D convolutional layer is typically used for RGB images in neural network.
I read that it is possible to use 3D conv layer.
What I do not understand is the math behind it.
Say your image is 300 by 300, and the kernel_size = (3, 3) and filt... |
H: Is applying pre-trained model on a different type of corpus called transfer learning?
I trained my classification model on corpus A and evaluated it on corpus B.
I do it, because for corpus A I have a lot more labeled sentences than for B. Nature of sentences used in A is different than sentences using in B. A has ... |
H: batch_size in neural network
When NN is construsted, batch size is not defined and place holder is used and its summary(tensorfow) shows the batch size as None.
This is useful because you can change batch size later.
In case of a simple model with 10 input features, 1 hidden layers with 10 neurons and output layer,... |
H: Getting NN weights for every batch / epoch from Keras model
I am trying to get weights for every batch / epoch from Keras model after it is trained. To do so I use callback to make model save weights during training. Yet after model is trained it looks like I get weights only from the final epoch. How to get all we... |
H: Ranking problem and imbalanced dataset
I know about the problems that imbalanced dataset will cause when we are working on classification problems. And I know the solution for that including undersampling and oversampling.
I have to work on a Ranking problem(Ranking hotels and evaluate based on NDCG50 score this li... |
H: Why use gradient descent on Deep Nets / RNNs when cost function is not convex?
Why do we use gradient descent on very non-convex loss functions such as in Deep nets / RNNs rather than a heuristic search (genetic algorithms, simulated annealing, etc)?
AI: Even if your cost function is not convex, any minimization al... |
H: Metric to use to choose between different models - Hyperparameters tuning
I'm building a Feedforward Neural Network with Pytorch and doing hyperparameters tuning using Ray Tune. I have train, validation and test set, train and validation used during the training procedure.
I have different versions of the model (di... |
H: What is the appropriate statistical significance test for multi-class classification?
I have a multi-class classification problem. I am primarily using macro-average F1 measure to evaluate the performance of models and want to verify if the results are statistically significant. I have the results of two classifier... |
H: What is the difference between GPT blocks and Transformer Decoder blocks?
I know GPT is a Transformer-based Neural Network, composed of several blocks. These blocks are based on the original Transformer's Decoder blocks, but are they exactly the same?
In the original Transformer model, Decoder blocks have two atten... |
H: Why does horizontal lines in plt.plot(feature, '.') mean that the data have been properly shuffled?
I am following a Mooc and in this lecture about visualisation in explenatory data analysis the lecturer claims that when plotting the row indexes against feature values, if we have lines on the feature value axis it ... |
H: Encoding Tags for Random Forest
I have the following data set:
I want to use attributes Tags and Authors to classify each record into their respective Rating. In order to do so I want to use a random forest classifier. My concern is how to deal with Tags attribute. Each of the entry has an undetermined number of t... |
H: Gradient descent does not converge in some runs and converges in other runs in the following simple Keras network
When training a simple Keras NN (1 input, 1 level with 1 unit for a regression task) during some runs I get big constant loss that does not change in 80 batches. During other runs it decreases. What may... |
H: From where does BERT get the tokens it predicts?
When BERT is used for masked language modeling, it masks a token and then tries to predict it.
What are the candidate tokens BERT can choose from? Does it just predict an integer (like a regression problem) and then use that token? Or does it do a softmax over all po... |
H: How to choose between different types of feature scaling?
The feature set for my multi-class multi-label classification task, using the MLPClassifier from scikit learn, contains mostly features where the values are in the same range of [0,1], but there are 3 out of 45 features where this isn't the case and feature ... |
H: One-hot vector for fixed vocabulary
given a vocabulary with $|V|=4$ and V = {I, want, this, cat} for example.
How does the bag-of-words representation with this vocabulary and one-hot encoding look like regarding example sentences:
You are the dog here
I am fifty
Cat cat cat
I suppose it would look like this
$V_... |
H: What's the difference between sequence preprocessing and text preprocessing in Keras?
In Keras, we mainly have three types of preprocessing, i.e., sequence preprocessing, text preprocessing, and image preprocessing. However, for me, I think the meanings of the word "sequence" and "text" are the same. How to underst... |
H: Generate new features from two columns
I have database with three columns, y,x1 and x2:
>>>y x1 x2
0 0.25 -19.3 -25.1
1 0.24 -18.2 -26.7
2 0.81 -45.2 -31.4
...
I want to create more features based on the x columns.
until now I have just created random functions and tries to check their co... |
H: How would I approach training a model and encoding this categorical data
So I have the following data:
I have one series where each word has a value that describes the average review score that would get.
For example, if the word "excellent" showed up in reviews with a score of 2,3,5,4 it would gain a value of 3.5.... |
H: How pre-trained BERT model generates word embeddings for out of vocabulary words?
Currently, I am reading BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. I want to understand how pre-trained BERT generates word embeddings for out of vocabulary words? Models like ELMo process inputs... |
H: Dense Layer vs convolutional layer - when to use them and how
I know what's the difference between the two, however I am a little confused on to use them. I have also seen some models that have a mix of both. what's the logic behind it? or is it only random things?
AI: As known, the main difference between the Conv... |
H: What is the use of applying img_to_array() after cv2.imread()
In a book, I saw the following code to load images from a directory:
1.image = cv2.imread(imagePath)
2.image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
3.image = cv2.resize(image, (28,28))
4.image = img_to_array(image)
5.data.append(image)
cv... |
H: How do I transform a file to .txt file using pandas?
I have to submit a machine learning project, and it has to be node in a .txt file. I know that if I am using pandas, and I want to transform a file from another format to .csv format I can use .to_csv().
Is there something similar for transforming the file into .... |
H: ValueError: Input 0 of layer sequential is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 25, 25, 1]
I am trying to use conv1D but getting that error.
My dataset's is batched and has a shape of [None, 25, 25, 1]
I am using input_shape=(25,25)
I am not able to figure out what... |
H: What is the difference between AI, ML, NN and DL?
What is the difference between the following four categories:
Artificial Intelligence (AI)
Machine Learning (ML)
Neural Network (NN)
Deep Learning (DL)
Data Science
My current understanding is that each of these encapsulates some set of algorithms. I feel like, am... |
H: Pandas: Compare two Dataframe and Groupby categorical
My Question is about pandas DataFrame, I have two DataFrame both follow the same structure.
df_1:
Index Text Category
0 Text 01 1
1 Text 02 1
2 Text 03 1
3 Text 04 1
df_2:
Index Text Category... |
H: Can anyone recommend me a very good pre-trained model for face or head detection?
I really need to know the best pre-trained models to detect faces and/or peoples' head. Not a face recognition model, but only to classify whether an object is a person's head/face or not.
I'm implementing one on top of Resnet50 from ... |
H: How to find appropliate algorithm for natural language based two data
What I would like to do
I would like to create a model to infer nationality from name and created the below data frame combining two dataset from Kaggle.
AI: There's no algorithm intended specifically for this task, you need to design the process... |
H: Which term is correct Datafication or Datification?
I have recently started reading Introduction to Data Science: A python approach to Concepts, Techniques, and Applications and taking notes on Data Science.
Chapter 1 repeatedly uses the term Datification(the process of rendering into data aspects of the world tha... |
H: How to merge all the data to have a final dataset
I am working with a problem that has different tables, end goal to predict if a customer will end up subscribing based on purchases.
Mother table containing user_id,register_reason
|user_id|reason_reg|source|
|-------|----------|------|
| 1 | 2 | 3 |
|... |
H: Why I would use TF-IDF after Bag-of-Words (CountVectorizer)?
In my recent studies over Machine Learning NLP tasks I found this very nice tutorial teaching how to build your first text classifier:
https://towardsdatascience.com/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a
... |
H: Difference between Word Embedding and Text Embedding
I am working on a dataset of amazon alexa reviews and wish to cluster them in positive and negative clusters. I am using Word2Vec for vectorization so wanted to know the difference between Text Embedding and Word Embedding. Also, which one of them will be useful ... |
H: Why use regularization?
In a linear model, regularization decreases the slope. Do we just assume that fitting a lin model on training data overfits by almost always creating a slope which is higher than it would be with infinite observations instead? What is the intuition?
AI: Regularization is used to help smooth ... |
H: List value in Pandas DataFrame column makes analysis harder
Should I move to database?
I have a list of courses data in JSON format that looks like this:
courses = [
{
course_id: "c_01",
teachers: ["t_01", "t_02"]
},
{
course_id: "c_02",
teachers: ["t_02", "t_03"]
}
]
And a list of teacher... |
H: Can we identify that an academic dataset was used for commercial purpose
There are many datasets that are released on the internet. Authors of many of these datasets state that the datasets are strictly for academic usage and not for commercial purposes. Although some datasets are released for both academic and com... |
H: How to compute score and predict for outcome after N days
Let's say I have a medical dataset/EHR dataset that is retrospective and longitudinal in nature. Meaning one person has multiple measurements across multiple time points (in the past). I did post here but couldn't get any response. So, posting it here
This d... |
H: How to collect info about unseen bugs given user's comments/feedbacks?
I have a dataframe which looks like:
user_id, comment
0, 'Functional but Horrible UI'
1, 'Great everything works well'
2, 'I struggled finding plus button because of theme colors in dark mode'
3, 'Keeps stopping on Android 10'
4, 'I like the fun... |
H: Can neural networks have multi-dimensional output nodes?
I'm trying to understand what's possible with TensorFlow's output layer. Specifically, are outputs always a flat array?
Since a neuron (or 'unit', in TF) has just one number, and there is only one set of outputs, it seems that output must have a single dimens... |
H: Is it possible to use a pretrained scikit learn model to make predictions on a dataset with different features (than those used during training)
Say we have a model trained on dataset A, which has a number of features, as usual. We then persist that model to disk and use it when we need to run inference (make predi... |
H: TensorFlow 2 one-hot encoding of labels
I was following this basic TensorFlow Image Classification problem, where images of flowers have to be classified into one of 5 possible classes. The labels in the training set are not one-hot encoded, and are individual numbers: 1,2,3,4 or 5 (corresponding to 5 classes). The... |
H: Does GridSearchCV not save the best parameters?
So I tuned the hyperparameters using GridSearchCV, fitted the model to the data, and then used best_params_. I'm just curious why GridSearchCV takes too long to run best_params_, unlike RandomSearchCV where it instantly gives answers. The time it takes for GridSearchC... |
H: Twitter Data-Analyse: What can I do with the data?
I retrieve data to a specific topic from Twitter and did my sentiment analysis on it. I never did anything in NLP, etc. So what else can I do with that? "Main goal" would be to find out if the Twitter community is against this "topic" or not.
I am also struggling w... |
H: Why this TensorFlow Transformer model has Linear output instead of Softmax?
I am checking this official TensorFlow tutorial on a Transformer model for Portuguese-English translation.
I am quite surprised that when the Transformer is created, their final output is a Dense layer with linear activation, instead of Sof... |
H: sine, cosine transformed cyclical features - am I losing information?
If I use sine, cosine transformation for cyclical features (e.g. weekday or hour of the day), do I lose information if the first ordinal value was 0 respectively?
Assume hours of the day are encoded as follows: 0, 1, ..., 23
If I apply the sine, ... |
H: Differentiate between positive and negative clusters
I have applied k-means clustering on my dataset of Amazon Alexa reviews.
model = KMeans(n_clusters=2, max_iter=1000, random_state=True, n_init=50).fit(X=word_vectors.vectors.astype('double'))
Now I want to check which cluster is positive and which is negative, c... |
H: Use a GPU to speed up neural net training in R
I'm currently training a neural net model in R and am wanting to use a GPU to speed up this process. I've looked into this but it appears that this is unavailable to Mac users as Apple no longer uses NVIDIA GPUs.
Can anyone tell me if this is the case, and if not how ... |
H: distribution difference between image and text
Once for the task of image captioning I've read that, the features extracted from image and text by deep networks are from two different worlds and got different distribution. My question is how is the distribution in two of them and how are they different?
AI: Suppose... |
H: Role of decoder in Transformer?
I understand the mechanics of Encoder-Decoder architecture used in the Attention Is All You Need paper. My question is more high level about the role of the decoder. Say we have a sentence translation task: Je suis ètudiant -> I am a student
The encoder receives Je suis ètudiant as t... |
H: Why transform embedding dimension in sin-cos positional encoding?
Positional encoding using sine-cosine functions is often used in transformer models.
Assume that $X \in R^{l\times d}$ is the embedding of an example, where $l$ is the sequence length and $d$ is the embedding size. This positional encoding layer enco... |
H: What exactly is a dummy trap? Is dropping one dummy feature really a good practice?
So I'm going through a Machine Learning course, and this course explains that to avoid the dummy trap, a common practice is to drop one column. It also explains that since the info on the dropped column can be inferred from the othe... |
H: MSE relevance as a metric when errors < 1
I'm trying to build my first models for regression after taking MOOCs on deep learning. I'm currently working on a dataset whose labels are between 0 and 2. Again, this is a regression task, not classification.
The low y values imply that the loss for each sample is quite l... |
H: Does auto.arima of the forecast package deal with seasonality and trend automatically
I'm reading some code involving auto.arima method from the forecast package in R. What I'm curious is whether there is a necessity for decomposing the time series data into seasonal, trend and stochastic compoents before passing t... |
H: Can the use of EarlyStopping() offset overfitting problems caused by validation_split?
Keras gives users the option, while fitting a model, to split the data into train/test samples using the parameter "validation_split.
Example:
model = Sequential()
model.add(Dense(3, activation = 'relu'))
/// Compile model ///
... |
H: How to train a neural network on multiple objectives?
I have a multi-class neural network classifier that has K classes(products). For every row, only one of the classes will be 1 at a time. Now, this approach works fine if I have only 1 objective to optimize i.e Which of these N products was "clicked" by the user.... |
H: In sequence models, is it possible to have training batches with different timesteps each to reduce the required padding per input sequence?
I want to train an LSTM model with variable length inputs. Specifically I want to use as little padding as possible while still using minibatches.
As far as I understand each ... |
H: Why should I use data augmentation as Keras layer
I've seen in several Tensorflow/Keras tutorials that data augmentation functions are added as keras layers. When I converted my Keras Python model (for production purpose) to TensorflowJS I faced the issue that e.g. the RandomFlip layer is not available in Tensorflo... |
H: label encoding or one-hot encoding or none when using decision tree?
I've been learning about decision tree from multiple resources but still not fully understanding data preprocessing step.
from https://www.youtube.com/watch?v=PHxYNGo8NcI&t=535s&ab_channel=codebasics it uses decision tree with label encoder and in... |
H: Which are the worse machine learning models for text classifications?
I was looking at text classification, and for curiosity I was searching online for which were the best models for text classifications. About this, I found that they are linear support vector machines and naive bayes.
But which are the worse mode... |
H: Is there an inherent recency bias in deep learning?
When working with very large models within Deep Learning, training often takes long and requires small batch sizes due to memory restrictions.
Usually, we are left with a model checkpoint after training has commenced. I am wondering whether the exact time at which... |
H: Micro Average vs Macro Average for Class Imbalance
I have a dataset consisting of around 30'000 data points and 3 classes. The classes are imbalanced (around 5'000 in class 1, 10'000 in class 2 and 15'000 in class 3). I'm building a convolutional neural network model for classification of the data. For evaluation I... |
H: BERT minimal batch size
Is there a minimum batch size for training/re-fining a BERT model on custom data?
Could you name any cases where a mini batch size between 1-8 would make sense?
Would a batch size of 1 make sense at all?
AI: Small mini-batch size leads to a big variance in the gradients. In theory, with a su... |
H: How to store efficiently very large sparse 3D matrices
To train a CNN, I have stacked arrays of images over observations [observations x width x length]. The dataset is very sparse ($95\%$). What would be an efficient way of storing these matrices efficiently in terms of
format (e.g. pickle, parquet)
structure (e.... |
H: Attention for time-series in neural networks
Neural networks in many domains (audio, video, image text/NLP) can achieve great results.
In particular in NLP using a mechanism named attention (transformer, BERT) have achieved astonishing results - without manual preprocessing of the data (text documents).
I am intere... |
H: How to Present All Categories in All Samples
I have a data contains many categorical columns. When I sampled this data randomly a few times and applied one-hot encoding to categorical columns I noticed that it ended up with datasets with different column counts. Because not all categories in columns preserved in sa... |
H: Can we consider high correlation to be a good predictor?
The problem of predicting the daily number of COVID-19 cases is indeed challenging and many (external) factors should be taken into account to come up with a reasonable predictor. However, we have studied Twitter for a specific country (not English) for the p... |
H: Why does GPU speed up inference?
I understand that GPU can speed up training for each batch multiple data records can be fed to the network which can be parallelized for computation. However, for inference, typically, each time the network only processes one record, for instance, for text classification, only one t... |
H: Comparing TFIDF vectors of different shapes
I'm working on a project using TF-IDF vectors and agglomerative clustering -- the idea is that the corpus of documents increases over time, and when a new document is added, the mean cosine similarity with each cluster will be calculated to find the best match.
However, I... |
H: What is the difference between BERT architecture and vanilla Transformer architecture
I'm doing some research for the summarization task and found out BERT is derived from the Transformer model. In every blog about BERT that I have read, they focus on explaining what is a bidirectional encoder, So, I think this is ... |
H: Over-sampling: is my model over-fitting?
I would like to ask you some questions on how to consider (good or not) the following results:
OVER-SAMPLING
precision recall f1-score support
0.0 1.00 0.85 0.92 873
1.0 0.87 1.00 0.93 884
... |
H: How to set class_weight parameter for cost sensitive learning?
I'm dealing with a binary classification problem with a balanced data set, however false positives are much more costly than false negatives. Let's just say that an FP is in general 3 times more costly than an FN and the response variable = 1 means a po... |
H: Cause of periodic jumps in loss function
I might be missing something obvious as I am new to machine learning. I am training an SSD Inception V2 for detecting buildings from satellite images. I use the Tensorflow Object Detection API. I am having troubles interpreting why the value of loss seems to change periodica... |
H: making a picture of a neural network in tensorflow
I'm new to Machine Learning and I have just gone through a tutorial explaining how to create a neural network in TensorFlow. I was wondering if it is possible to visualize the neural network I created. The output should be a picture like this
Thanks.
MRE:
import te... |
H: What is the meaning of the sparsity parameter
Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be shrunk to zero. What is unclear to me is that how does this method decides w... |
H: How to deal with a binary classification problem, where the instances in the negative class are very similar?
Let's say, one wants to detect, whether a picture of a fixed size contains a cat or not. But as a dataset, you have 10000 pictures of cats, and 30000 pictures which don't contain a cat, but are very similar... |
H: Causal inference VS Active learning?
Imagine we have some lists of features that are changing in time. Each row of the list corresponds to a sample (Change in space). I would like to know whether machine learning is able to determine the effect of each sample on another sample. For instance, the target value for th... |
H: Why does my GPU immediately run out of memory when I try to run this code?
I am trying to write a neural network that will train on plays by Shakespeare and then write its own passages. I am using pytorch. For some reason, my GPU immediately runs out of memory. Note I am not running it on my own GPU; I am running i... |
H: Scheduler for activation layer parameter using Keras callback
I want to train a keras model with a custom activation layer.
The custom activation layer has one fixed non trainable parameter.
I want to change/set this non trainable parameter of all custom activation layers in model during training after few epochs.
... |
H: Predicting the likelihood that a prediction from a linear regression model is accurate
So to set up the problem:
I have a data set that had labeled data like colour, brand and quality as independent variables and the dependent is RRP (price).
I have made a linear regression model using this data and can predict the... |
H: Is there a quantitative way to determine if a class of algorithms tends produce low bias or low variance models?
I understand that some machine learning models tend to be low bias, whereas others tend to be low variance (source). As an example, a linear regression will tend to have low variance error and high bias... |
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