Does the Keras library allow the application of the learning process while working on the model for continuous optimization of its performance?
The Keras library, which serves as a high-level neural networks API, is widely utilized in the field of machine learning for its user-friendly interface and powerful features. It is fully compatible with backends such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). One of the fundamental aspects of machine learning is the iterative process of
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Introduction to Keras
What are the three components that need to be specified when compiling a Keras model?
When compiling a Keras model in the field of Artificial Intelligence, there are three essential components that need to be specified. These components play a important role in configuring the model for training and evaluation. By understanding and correctly specifying these components, one can effectively harness the power of Keras and advance in machine learning.
What are the activation functions used in the layers of the Keras model in the example?
In the given example of a Keras model in the field of Artificial Intelligence, several activation functions are used in the layers. Activation functions play a important role in neural networks as they introduce non-linearity, enabling the network to learn complex patterns and make accurate predictions. In Keras, activation functions can be specified for each
What are the steps involved in preprocessing the Fashion-MNIST dataset before training the model?
Preprocessing the Fashion-MNIST dataset before training the model involves several important steps that ensure the data is properly formatted and optimized for machine learning tasks. These steps include data loading, data exploration, data cleaning, data transformation, and data splitting. Each step contributes to enhancing the quality and effectiveness of the dataset, enabling accurate model training
What are the two ways to use Keras?
Keras is a high-level deep learning framework that provides a user-friendly interface for building and training neural networks. It is widely used in the field of artificial intelligence and has gained popularity due to its simplicity and flexibility. In this answer, we will discuss the two main ways to use Keras: the Sequential API and
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Introduction to Keras, Examination review
How is Keras described in terms of its design and functionality?
Keras is a high-level neural networks API that is written in Python. It is designed to be user-friendly, modular, and extensible, allowing users to quickly and easily build and experiment with deep learning models. Keras provides a simple and intuitive interface to build, train, and deploy deep learning models, making it a popular choice among
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Introduction to Keras, Examination review