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The two most widely-used open-source machine learning frameworks for training and building deep learning models are TensorFlow and PyTorch. These frameworks have unique differences in their approach to building and training models. The choice of frameworks depends on the user's specific needs and preferences.
Hugging Face offers solutions and tools for developers and researchers. With an open-source library called Transformers, they provide access to state-of-the-art pre-trained models.
Geospatial data refers to data that represents events, objects, or features on the Earth’s surface. Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms.
In 2022, Gartner named Microsoft Power BI the Business Intelligence and Analytics Platforms leader. With the aid of business intelligence tools, unstructured data can go through extraction, cleaning, and analysis processes to create insights that help organizations make data-driven decisions.
Using deep neural networks it is possible to do this in a smooth manner. You can create two subnetworks: one network reads the daily data, the other network reads monthly data. The outputs of the two networks are then joined together, before they are passed in another layer.
In this post, we will talk about encoding to be able to use categorical data as features for our ML models. Categorical data has variables that contain label values (text) and not numerical values. We have to convert data which contains categorical variables to numbers before we can train a ML model. Two most popular encoding techniques are Ordinal Encoding and One-Hot Encoding.
Human behaviour describes how people behave and interact. It is based on and affected by a number of elements, including genetic makeup, culture, personal values and attitudes.
Computer vision is quite important in the field of artificial intelligence. It enables computers to replicate the human visual system and it uses information from images & videos to identify and classify objects.
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