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Brief conclusions
Convergence in ML is a term used when training a model. Convergence describes how a function changes during training.
The algorithm converges when the loss function reaches a minimum or values ​​close to it, and the parameters and metrics stop changing actively.
If convergence is achieved, it means that the model has reached the optimal learning point and the process can be stopped.
It happens that the algorithm diverges - never reaches the convergence point. This happens due to incorrectly selected parameters, inappropriate data, errors in the model architecture.
To speed up convergence, ML uses data normalization, modifies norway telegram data learning algorithms, and selects optimal hyperparameter values.Seasonal demand: when is the best time to look for a job in IT?


Analysis
November 21, 2024

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Seasonal demand: when is the best time to look for a job in IT?
Content

1.
Demand for IT specialists in 2024
2.
Which IT specialists are most in demand?
Skillfactory Media Editorial
Skillfactory Media Editorial
Honest stories about careers in IT

Despite the shortage of personnel, finding a job in IT is not always easy. The industry has seasonal demand: periods when there are the most vacancies on the market and there is plenty to choose from. What are these periods? What kind of IT specialists are actively sought right now? Let's find out with a career expert.