One of the simplest ways to get started with TensorFlow, a machine learning library by Google is to build an image classifier from scratch…
Callbacks are an important type of object in Keras and TensorFlow. They are designed to be able to monitor the model performance in metrics at certain points in the training run and perform some actions that might depend on those performances in metric values.
Keras has provided a number of built-in callbacks, for example,
LearningRateScheduler etc. Apart from these popular built-in callbacks, there is a base class called
Callback which allows us to create our own callbacks and perform some custom actions. In this article, you will learn what is the
Callback base class, what it can do, and how to build your own callbacks.
If you want to learn more about those built-in callbacks, please check out the following article:
In this Neural Networks and Deep Learning Tutorial, we will talk Image Preprocessing In Neural Networks and Deep Learning with Keras and TensorFlow. First of all, we will cover what image preprocessing is and why it is used before passing the images through our neural networks.
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial: How to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more!
This video on TensorFlow and Keras tutorial will help you understand Deep Learning frameworks, what is TensorFlow, TensorFlow features and applications, how TensorFlow works, TensorFlow 1.0 vs TensorFlow 2.0, TensorFlow architecture with a demo. Then we will move into understanding what is Keras, models offered in Keras, what are neural networks and they work.
In this Neural Network and Deep Learning Tutorial, we are going to talk about Data Augmentation with Keras and TensorFlow. First of all, we are going to talk about what data augmentation is and why we should use it when working with neural networks.
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