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
What is overfitting in machine learning and why does it occur?
Overfitting is a common problem in machine learning where a model performs extremely well on the training data but fails to generalize to new, unseen data. It occurs when the model becomes too complex and starts to memorize the noise and outliers in the training data, instead of learning the underlying patterns and relationships. In
- 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 can overfitting be visualized in terms of training and validation loss?
Overfitting is a common problem in machine learning models, including those built using TensorFlow. It occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. This leads to poor generalization and high training accuracy, but low validation accuracy. In terms of training and validation loss,
- 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 is overfitting in machine learning models and how can it be identified?
Overfitting is a common problem in machine learning models that occurs when a model performs extremely well on the training data but fails to generalize well on unseen data. In other words, the model becomes too specialized in capturing the noise or random fluctuations in the training data, rather than learning the underlying patterns or
- 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
What are some of the drawbacks of using deep neural networks compared to linear models?
Deep neural networks have gained significant attention and popularity in the field of artificial intelligence, particularly in machine learning tasks. However, it is important to acknowledge that they are not without their drawbacks when compared to linear models. In this response, we will explore some of the limitations of deep neural networks and why linear
What is the purpose of training the model in machine learning?
Training the model is a important step in machine learning as it is the process by which the model learns from the data and improves its ability to make accurate predictions or classifications. The purpose of training the model is to optimize its performance by adjusting its internal parameters based on the training data. This