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What do you mean exactly with competitive as a data scientist? Unfortunately, many employers will have different expectations of someone they hire to be a Data Scientist, so there isn't a single answer! In any case, I think it is a good idea to know three components to be effective with databases: Managing a connection: how to create and connect to a ...


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is it even possible to "un-learn" a single training example without retraining on the new data? To the best of my knowledge, the answer is no except in some very special cases. The most obvious exception that comes to mind is instance-based learning, such as kNN: since the "model" itself consists only of the set of training instances, it's straightforward ...


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Here's the solution that works every time and very efficiently. A) Case of file import torchvision torchvision.datasets.utils.download_file_from_google_drive(file_id, root, filename=None, md5=None) This download a Google Drive file and place it in root. Args: - file_id (str): id of file to be downloaded - root (str): Directory to place downloaded ...


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This solved the problem: select distinct on (customer) customer, agent, min(call) from call_data; I am using PostgreSQL.


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Obtaining a dataset is an important part of defining a ML problem but it's not the only one. Typically this involves the following steps: Define the goal of the problem. Example: predict AZT level of tolerance among AIDS patients. Obtain appropriate data for the problem. Design the formal setting of the experiment: what kind of problem is it (e.g. ...


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For large data sets the limitations of available memory prevents you from loading all the data simultaneously. What is typically done is to provide your data to the network in batches. Batches are simply groupings of your data. The batch size maximum value is limited by available memory. If you use Keras the ImageDataGenerator.flow from directory provides a ...


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I suggest you to use as much data as possible. If the images are coming from two different cameras, it could be an original way to fight overfitting. I would use both datasets, and operate a train-validation-test split that is transversal to both. Additionally, since the dataset is small, I strongly enourage you to use lots and lots of data augmentation, ...


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It is a very good point, and IMHO it is often overlooked and underestimated by Data Scientists. I have come to believe it strongly depends on the following variables that are largely intertwined (solely based on personal experience): Domain (e-Commerce, Manufacturing, etc.): I have witnessed that majority of e-Commerce, realtors, online businesses are ...


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