Optimizers, Loss Functions and Learning Rate in Neural Networks with Keras and TensorFlow

Optimizers, Loss Functions and Learning Rate in Neural Networks with Keras and TensorFlow

In this Neural Networks Tutorial, we will talk about Optimizers, Loss Function, and Learning rate in Neural Networks. I will explain what an optimizer is and how it is used in the training process of a neural network.

In this Neural Networks Tutorial, we will talk about Optimizers, Loss Function, and Learning rate in Neural Networks. I will explain what an optimizer is and how it is used in the training process of a neural network. We are going to see some different types of optimizers and loss functions. The objective of a neural network during training is to minimize the loss function with an optimizer with a chosen learning rate. At the end of the video, we are going to compile the neural network that we have created with the chosen optimizer and loss function. Throughout this tutorial, we are going to cover everything about the basics and fundamentals of neural networks. How they work, how you can create one yourself, and how you can train it to make actual predictions on data the network has not seen before.

Code examples and images from this tutorial will be available on my GitHub: https://github.com/niconielsen32​

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