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This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the values of these features, and then training a model for each bin. They are then examining the differences between the models. Usually this is done to learn ...


3

The most common way people deal with inputs of varying length is padding. You first define the desired sequence length, i.e. the input length you want your model yo have. Then any sequences with a shorter length than this are padded either with zeros or with special characters so that they reach the desired length. If an input is larger than your desired ...


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It all depends on the quality of data. Due to old rule "Garbage in, garbage out" link , if you have bad quality data(data redundancy, unstructured data, too much memory, etc) your results won't be spectacular. In other cases, everybody could be a Data Scientist, because its only task was "put raw text into classifier". Also, you should remember that BERT ...


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I answered a similar question earlier and here is a piece of my answer that i think covers your question: Batch normalization's assistance to neural networks wasn't really understood for the longest time, initially it was thought to assist with internal covariate shift (hypothesized by the initial paper: Batch Normalization: Accelerating Deep Network ...


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Here's a list of some of the best python libraries for natural language processing. Natural Language Toolkit (nltk) Covers all the basic functions and NLP tools such as tokenization etc. TextBolb This is a good library of beginners, it provides the nltk toolkit in a simplified format. Spacy It is an advanced library and can be used in production code. You ...


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manual feature engineering started becoming obsolete That is wrong. Any suggestion on when to use manual feature engineering, feature learning or a combination of the two? Deep learning is awesome for natural signals like images, audio or large amounts of unstructured text (e.g. arbitrary crawled websites) There are some basic steps that make almost ...


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It is much simpler to process the data in a different way. Since you're using temporal data a common practice is to define a priori a minimum time-step, usually called $\textit{granularity}$, which must be bigger than you're sensor responsiveness. Using this granularity value you'll then be able split your data into intervals, and you can then combine each ...


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While I can see that there are some heuristics that can tell you whether an entry is 'weird', I don't see any way that you can correct this. Where would you get a correct value from? I would perhaps start with a statistical analysis, looking at the distribution of values to get an idea of the state the data is in. From this you can then already see some ...


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Data preprocessing consists of all those techniques used to generate the final datasets (with an appropriate size, structure, and format) for the machine learning algorithms or models. Data acquisition should not be part of data preprocessing, but the step preceding it, which gathers the raw data (which may e.g. be noisy). The book Data Preprocessing in ...


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Your question are missing some details and i will assume some scenarios. If you have a classification problem: you can try group the values in intervals that make sense (you should analyze and decide for this setup), if its possible. For example: 0.000-0.250 (0), 0.251-0.500 (1), 0.501-0.750 (2) and so on. Note that neural networks are sensible for distance ...


1

As you mentioned in the comments about a possible problem of using mean, median type of imputations naively could lead to wrong predictions. In such cases, you need to first check whether you have enough data. If you have enough data You can try using MICE (Multivariate Imputation By Chained Equations) algorithm on your missing data. The method is based ...


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It looks like everything you want is available with the Deep Learning Toolkit (DLTK) for Medical Imaging There is also a blog: An Introduction to Biomedical Image Analysis with TensorFlow and DLTK There is a DataCamp course that walks you through most of the process but instead of a classifier they use deep learning to reconstruct brain images. They ...


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You can use append function: final = df1.append(df2, ignore_index=True) To set the last column as labels, you set them as so by: labels = np.array(final["will_buy"]) So, when calling the fit method on the model you build, you set labels = labels.


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It depends how you recognise the entities. If you do a simple gazetteer lookup, then it could be faster, as you have fewer tokens to deal with. However, if you use contextual rules, then stop words might be vital to identify certain contexts, so by removing stop words you lose information about the entity's environment. For example, if [work] at {...


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In fact, choosing smartly the values of the image augmentation can help the performance of your system. Where I work we developed an object detector for cars. We had the following image augmentation parameters: Apect ratio distorsion: it changed the cars dimensions Additive noise: it blurred the image Change colorspace: change the cars colors Saturation ...


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You can use the function inverse_transform of the created MinMaxScaler object. See also this Stack Overflow question for other answers and examples.


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Depends on how your game is played. Is there any meaning assigned to the order of cards, or are all 5 played simultaneously? If order matters, use 5 one-hot vectors so you can choose how to order them, otherwise use a single 5-hot input vector. I would also add that if temporal order matters, you could also use a recurrent net with a 52-element input and ...


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I developed a python script to crop faces using MTCNN. I found this to be the most accurate of all the face cropping algorithms at the expense of being somewhat slower. The function I developed is on the kaggle website at https://www.kaggle.com/gpiosenka/detect-align-resize-rename-facial-images. The markup first cell explains how to use it. In a nutshell ...


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I have found a script that does what i need: https://github.com/leblancfg/autocrop


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I don't know of a tool but you could write a simple script to detect faces and crop it. It's quite simple with the Haar cascade in openCV to detect faces and use inbuilt functions to crop your image based on the size of the detected face. Hope that helps !


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In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations. We do feature scaling to make our model robust to outliers and make an initial impact of every feature on the model will be roughly similar Graphical-model based classifiers, ...


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No, there is no computational advantage of the second method over the first, if you neglect the computational requirements for the calculation of $\sigma$ and $\mu$. We generally use the first method for better results. This is because if you separate your dataset into train and test data, then you may normalise the train data perfectly between $0$ and $1$ ...


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