Where is the information about a neural network model stored (including parameters and hyperparameters)?
In the domain of artificial intelligence, particularly concerning neural networks, understanding where information is stored is important for both model development and deployment. A neural network model consists of several components, each of which plays a distinct role in its operation and efficacy. Two of the most significant elements within this framework are the model's
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the difference between hyperparameters and model parameters?
In the realm of machine learning, distinguishing between hyperparameters and model parameters is important for understanding how models are trained and optimized. Both types of parameters play distinct roles in the model development process, and their correct tuning is essential for the efficacy and performance of a machine learning model. Model parameters are the internal
What is the role of the `model.json` file in the TensorFlow.js model folder?
The `model.json` file plays a important role in the TensorFlow.js model folder when importing a Keras model into TensorFlow.js. It serves as a metadata file that contains important information about the structure and parameters of the model. This file is generated during the conversion process from Keras to TensorFlow.js and is essential for correctly loading
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Importing Keras model into TensorFlow.js, Examination review