What is the relationship between a number of epochs in a machine learning model and the accuracy of prediction from running the model?
The relationship between the number of epochs in a machine learning model and the accuracy of prediction is a important aspect that significantly impacts the performance and generalization ability of the model. An epoch refers to one complete pass through the entire training dataset. Understanding how the number of epochs influences prediction accuracy is essential
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 1
What is the purpose of using epochs in deep learning?
The purpose of using epochs in deep learning is to train a neural network by iteratively presenting the training data to the model. An epoch is defined as one complete pass through the entire training dataset. During each epoch, the model updates its internal parameters based on the error it makes in predicting the output
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Advancing with deep learning, Model analysis, Examination review
What were the differences between the baseline, small, and bigger models in terms of architecture and performance?
The differences between the baseline, small, and bigger models in terms of architecture and performance can be attributed to variations in the number of layers, units, and parameters used in each model. In general, the architecture of a neural network model refers to the organization and arrangement of its layers, while performance refers to how
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 2, Examination review
How does underfitting differ from overfitting in terms of model performance?
Underfitting and overfitting are two common problems in machine learning models that can significantly impact their performance. In terms of model performance, underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor predictive accuracy. On the other hand, overfitting happens when a model becomes too complex
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 2, Examination review
Explain the concept of underfitting and why it occurs in machine learning models.
Underfitting is a phenomenon that occurs in machine learning models when the model fails to capture the underlying patterns and relationships present in the data. It is characterized by high bias and low variance, resulting in a model that is too simple to accurately represent the complexity of the data. In this explanation, we will
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 1, Examination review
What were the deviations observed in the model's performance on new, unseen data?
The performance of a machine learning model on new, unseen data can deviate from its performance on the training data. These deviations, also known as generalization errors, arise due to several factors in the model and the data. In the context of AutoML Vision, a powerful tool provided by Google Cloud for image classification tasks,
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, AutoML Vision - part 2, Examination review