Why do we need to apply optimizations in machine learning?
Optimizations play a important role in machine learning as they enable us to improve the performance and efficiency of models, ultimately leading to more accurate predictions and faster training times. In the field of artificial intelligence, specifically advanced deep learning, optimization techniques are essential for achieving state-of-the-art results. One of the primary reasons for applying
Is it possible to train machine learning models on arbitrarily large data sets with no hiccups?
Training machine learning models on large datasets is a common practice in the field of artificial intelligence. However, it is important to note that the size of the dataset can pose challenges and potential hiccups during the training process. Let us discuss the possibility of training machine learning models on arbitrarily large datasets and the
Is testing a ML model against data that could have been previously used in model training a proper evaluation phase in machine learning?
The evaluation phase in machine learning is a critical step that involves testing the model against data to assess its performance and effectiveness. When evaluating a model, it is generally recommended to use data that has not been seen by the model during the training phase. This helps to ensure unbiased and reliable evaluation results.
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Is it necessary to use other data for training and evaluation of the model?
In the field of machine learning, the use of additional data for training and evaluation of models is indeed necessary. While it is possible to train and evaluate models using a single dataset, the inclusion of other data can greatly enhance the performance and generalization capabilities of the model. This is especially true in the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Is it correct that if dataset is large one needs less of evaluation, which means that the fraction of the dataset used for evaluation can be decreased with increased size of the dataset?
In the field of machine learning, the size of the dataset plays a important role in the evaluation process. The relationship between dataset size and evaluation requirements is complex and depends on various factors. However, it is generally true that as the dataset size increases, the fraction of the dataset used for evaluation can be
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
How to recognize that model is overfitted?
To recognize if a model is overfitted, one must understand the concept of overfitting and its implications in machine learning. Overfitting occurs when a model performs exceptionally well on the training data but fails to generalize to new, unseen data. This phenomenon is detrimental to the model's predictive ability and can lead to poor performance
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
Is it possible to reuse training sets iteratively and what impact does that have on the performance of the trained model?
Iteratively reusing training sets in machine learning is a common practice that can have a significant impact on the performance of the trained model. By repeatedly using the same training data, the model can learn from its mistakes and improve its predictive capabilities. However, it is essential to understand the potential advantages and disadvantages of
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
When does overfitting occur?
Overfitting occurs in the field of Artificial Intelligence, specifically in the domain of advanced deep learning, more specifically in neural networks, which are the foundations of this field. Overfitting is a phenomenon that arises when a machine learning model is trained too well on a particular dataset, to the extent that it becomes overly specialized
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Neural networks, Neural networks foundations
Why too long neural network training leads to overfitting and what are the countermeasures that can be taken?
Training Neural Network (NN), and specifically also a Convolutional Neural Network (CNN) for an extended period of time will indeed lead to a phenomenon known as overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers. This results in a model that performs
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