Neural Networks Summary: All hyperparameters

The correct hyperparameter settings are critical to the success of a Feedforward Neural Network. In this video we take a high-level look on all main hyperparameters of Neural Networks. We see where in the lifecycle of the NNs they belong, what they mean and also how to set them using Python and Keras.
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Intro 00:00
Input & output layers 01:01
Hidden layers 03:48
Activation functions 04:57
Weight initialization 06:34
Regularization 07:52
Loss functions 10:21
Optimization algorithm & learning rate 11:14
Batch size & Epochs (Number of iterations) 13:13
Wrap-up 16:12
Keras weight initializers: keras.io/api/layers/initializ...
Keras regularizers: keras.io/api/layers/regulariz...
Keras loss functions: keras.io/api/losses/
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straightforward yet effective explanation, Thank you

[@AlexKashie]

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[@thompsono.4540]

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[@AssemblyAI]

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[@BrianPondiGeoGeek]

Great explanation

[@linghaoyi]

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[@shortslife8600]

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[@sundae1996]

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[@AssemblyAI]

Happy to hear you liked it! - Mısra

[@navidabasi]

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[@arminkashani5695]

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[@AssemblyAI]

You are very welcome

[@thaarapillai]

Beautifully explained!

[@AssemblyAI]

Thank you Thaara! - Mısra

[@shortslife8600]

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[@guimaraesalysson]

Very helpful

[@AssemblyAI]

Glad you think so!

[@zohaib319]

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[@AssemblyAI]

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[@jamesjulius7726]

thank you

[@AssemblyAI]

You're welcome

[@omgavy]

Great thanks, But didn't understood the validation part from 15:56

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