What will hapen if the test sample is 90% while evaluation or predictive sample is 10%?
In the realm of machine learning, particularly when utilizing frameworks such as Google Cloud Machine Learning, the division of datasets into training, validation, and testing subsets is a fundamental step. This division is critical for the development of robust and generalizable predictive models. The specific case where the test sample constitutes 90% of the data
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What role does dropout play in preventing overfitting during the training of a deep learning model, and how is it implemented in Keras?
Dropout is a regularization technique used in the training of deep learning models to prevent overfitting. Overfitting occurs when a model learns the details and noise in the training data to the extent that it performs poorly on new, unseen data. Dropout addresses this issue by randomly "dropping out" a proportion of neurons during the
Will too long neural network training lead to overfitting?
The notion that prolonged training of neural networks inevitably leads to overfitting is a nuanced topic that warrants a comprehensive examination. Overfitting is a fundamental challenge in machine learning, particularly in deep learning, where a model performs well on training data but poorly on unseen data. This phenomenon occurs when the model learns not just
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Data, Datasets
What is an optimal strategy to find the right training time (or number of epochs) for a neural network model?
Determining the optimal training time or number of epochs for a neural network model is a critical aspect of model training in deep learning. This process involves balancing the model's performance on the training data and its generalization to unseen validation data. A common challenge encountered during training is overfitting, where the model performs exceptionally
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Data, Datasets
How do pooling layers, such as max pooling, help in reducing the spatial dimensions of feature maps and controlling overfitting in convolutional neural networks?
Pooling layers, particularly max pooling, play a important role in convolutional neural networks (CNNs) by addressing two primary concerns: reducing the spatial dimensions of feature maps and controlling overfitting. Understanding these mechanisms requires a deep dive into the architecture and functionality of CNNs, as well as the mathematical and conceptual underpinnings of pooling operations. Reducing
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced computer vision, Convolutional neural networks for image recognition, Examination review
How do regularization techniques like dropout, L2 regularization, and early stopping help mitigate overfitting in neural networks?
Regularization techniques such as dropout, L2 regularization, and early stopping are instrumental in mitigating overfitting in neural networks. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization to new, unseen data. Each of these regularization methods addresses overfitting through different mechanisms, contributing to
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Neural networks, Neural networks foundations, Examination review
What is the purpose of max pooling in a CNN?
Max pooling is a critical operation in Convolutional Neural Networks (CNNs) that plays a significant role in feature extraction and dimensionality reduction. In the context of image classification tasks, max pooling is applied after convolutional layers to downsample the feature maps, which helps in retaining the important features while reducing computational complexity. The primary purpose
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, TensorFlow.js, Using TensorFlow to classify clothing images
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
Does increasing of the number of neurons in an artificial neural network layer increase the risk of memorization leading to overfitting?
Increasing the number of neurons in an artificial neural network layer can indeed pose a higher risk of memorization, potentially leading to overfitting. Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on unseen data. This is a common problem
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Overfitting and underfitting problems, Solving model’s overfitting and underfitting problems - part 1
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