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README.md

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@@ -27,15 +27,13 @@ Consider using TopicNet if:
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<div align="center">
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<img src="./docs/readme_images/training_scheme_example.png" width="60%" alt/>
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</div>
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<div align="center">
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<em>
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Example of the two-stage experiment scheme.
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At the first stage, regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\tau"> taking values in some range <img src="https://render.githubusercontent.com/render/math?math=\{\tau_1, \tau_2, \tau_3\}"> is applied.
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Best models after the first stage are <em>Model 1</em> and <em>Model 2</em> — so <em>Model 3</em> is not taking part in the training process anymore.
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The second stage is connected with another regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\xi"> taking values in range <img src="https://render.githubusercontent.com/render/math?math=\{\xi_1, \xi_2\}">.
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As a result of this stage, two descendant models of <em>Model 1</em> and two descendant models of <em>Model 2</em> are obtained.
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</em>
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</div>
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<em>
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Example of the two-stage experiment scheme.
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At the first stage, regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\tau"> taking values in some range <img src="https://render.githubusercontent.com/render/math?math=\{\tau_1, \tau_2, \tau_3\}"> is applied.
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Best models after the first stage are <em>Model 1</em> and <em>Model 2</em> — so <em>Model 3</em> is not taking part in the training process anymore.
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The second stage is connected with another regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\xi"> taking values in range <img src="https://render.githubusercontent.com/render/math?math=\{\xi_1, \xi_2\}">.
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As a result of this stage, two descendant models of <em>Model 1</em> and two descendant models of <em>Model 2</em> are obtained.
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</em>
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</p>
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