What are the limitations in working with large datasets in machine learning?
When dealing with large datasets in machine learning, there are several limitations that need to be considered to ensure the efficiency and effectiveness of the models being developed. These limitations can arise from various aspects such as computational resources, memory constraints, data quality, and model complexity. One of the primary limitations of installing large datasets
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, GCP BigQuery and open datasets
Can A regular neural network be compared to a function of nearly 30 billion variables?
A regular neural network can indeed be compared to a function of nearly 30 billion variables. To understand this comparison, we need to consider the fundamental concepts of neural networks and the implications of having a vast number of parameters in a model. Neural networks are a class of machine learning models inspired by the
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Introduction, Introduction to deep learning with Python and Pytorch
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