What are some more detailed phases of machine learning?
The phases of machine learning represent a structured approach to developing, deploying, and maintaining machine learning models. These phases ensure that the machine learning process is systematic, reproducible, and scalable. The following sections provide a comprehensive overview of each phase, detailing the key activities and considerations involved. 1. Problem Definition and Data Collection Problem Definition
Should separate data be used in subsequent steps of training a machine learning model?
The process of training machine learning models typically involves multiple steps, each requiring specific data to ensure the model's effectiveness and accuracy. The seven steps of machine learning, as outlined, include data collection, data preparation, choosing a model, training the model, evaluating the model, parameter tuning, and making predictions. Each of these steps has distinct
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
Does a proper approach to neural networks require a training dataset and an out-of-sample testing dataset, which have to be fully separated?
In the realm of deep learning, particularly when employing neural networks, the proper handling of datasets is of paramount importance. The question at hand pertains to whether a proper approach necessitates both a training dataset and an out-of-sample testing dataset, and whether these datasets need to be fully separated. A fundamental principle in machine learning
Is the out-of-sample loss a validation loss?
In the realm of deep learning, particularly in the context of model evaluation and performance assessment, the distinction between out-of-sample loss and validation loss holds paramount significance. Understanding these concepts is important for practitioners aiming to comprehend the efficacy and generalization capabilities of their deep learning models. To consider the intricacies of these terms, it
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Introduction, Introduction to deep learning with Python and Pytorch
How can one detect biases in machine learning and how can one prevent these biases?
Detecting biases in machine learning models is a important aspect of ensuring fair and ethical AI systems. Biases can arise from various stages of the machine learning pipeline, including data collection, preprocessing, feature selection, model training, and deployment. Detecting biases involves a combination of statistical analysis, domain knowledge, and critical thinking. In this response, we
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Machine learning algorithms can learn to predict or classify new, unseen data. What does the design of predictive models of unlabeled data involve?
The design of predictive models for unlabeled data in machine learning involves several key steps and considerations. Unlabeled data refers to data that does not have predefined target labels or categories. The goal is to develop models that can accurately predict or classify new, unseen data based on patterns and relationships learned from the available
Why the evaluation is 80% for training and 20% for evaluating but not the opposite?
The allocation of 80% weightage to training and 20% weightage to evaluating in the context of machine learning is a strategic decision based on several factors. This distribution aims to strike a balance between optimizing the learning process and ensuring accurate evaluation of the model's performance. In this response, we will consider the reasons behind
What is the purpose of separating data into training and testing datasets in deep learning?
The purpose of separating data into training and testing datasets in deep learning is to evaluate the performance and generalization ability of a trained model. This practice is essential in order to assess how well the model can predict on unseen data and to avoid overfitting, which occurs when a model becomes too specialized to
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Data, Datasets, Examination review
How do we separate a chunk of data as the out-of-sample set for time series data analysis?
To perform time series data analysis using deep learning techniques such as recurrent neural networks (RNNs), it is essential to separate a chunk of data as the out-of-sample set. This out-of-sample set is important for evaluating the performance and generalization ability of the trained model on unseen data. In this field of study, specifically focusing