Скачать или смотреть Deep neural networks step by step initialization

Welcome to another tutorial. In the last tutorial series, we wrote 2 layers of neural networks model, now it's time to build a deep neural network, where we could have whatever count of layers we want.

So the same as in previous tutorials at first we'll implement all the functions required to build a deep neural network. Then we will use these functions to build a deep neural network for image classification (cats vs dogs).

In this tutorial series, I will use non-linear units like ReLU to improve our model. Our deep neural network model will be built in a way, that we could easily define deep layers that our model would be easy-to-use.

This is a continuation tutorial of my one hidden layer neural network tutorial, so I will use the same data-set. If you are seeing this tutorial first time, cats vs dogs data set you can get from my GitHub page. If you don't know how to use data-set, check my previous tutorials.

Same as in previous tutorials, to build a deep neural network and we will be implementing several "helper functions". These helper functions will be used to build a two-layer neural network and an L-layer neural network. In each small helper function, we will implement, I will try to give you a detailed explanation.

Text version tutorials: https://pylessons.com/Deep-neural-net...
GitHub code: https://github.com/pythonlessons/Logi...
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