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I am just starting to work with Flux.jl and I want to understand how to build and train neural networks properly. What are the typical difficulties that beginners face? For example, how to prepare data, create a model and set up training?
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Working with the neural network in the Flux.1 library requires an understanding of several key points. It is important, for example, to learn the basics of programming in Julia, the language on which the library is built. For beginners, it is helpful to start with simple examples like linear regression or simple MNIST image classification to get comfortable with the syntax and basic concepts.
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An important feature of Flux.1 is its support for automatic differentiation, which makes it easy to compute gradients for back propagation of errors. If training is slow or the model converges poorly, try reducing the optimization step size or increasing the number of training epochs. Also remember to check the accuracy of the model on test data to avoid overtraining. Thus, learning the basics step by step and applying them in practice will gradually open your eyes to the vast possibilities of working with deep neural networks in Flux.1. And this api to create images in flux.1 ai will also help you a lot
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в мене друг таким займається, спитаю поради
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