How can one detect biases in machine learning and how can one prevent these biases?
Detecting biases in machine learning models is a crucial 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
Are batch size, epoch and dataset size all hyperparameters?
Batch size, epoch, and dataset size are indeed crucial aspects in machine learning and are commonly referred to as hyperparameters. To understand this concept, let's delve into each term individually. Batch size: The batch size is a hyperparameter that defines the number of samples processed before the model's weights are updated during training. It plays
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Can TensorBoard be used online?
Yes, one can use TensorBoard online for visualizing machine learning models. TensorBoard is a powerful visualization tool that comes with TensorFlow, a popular open-source machine learning framework developed by Google. It allows you to track and visualize various aspects of your machine learning models, such as model graphs, training metrics, and embeddings. By visualizing these
Where can one find the Iris data set used in the example?
To find the Iris dataset used in the example one can access it through the UCI Machine Learning Repository. The Iris dataset is a commonly used dataset in the field of machine learning for classification tasks, particularly in educational contexts due to its simplicity and effectiveness in demonstrating various machine learning algorithms. The UCI Machine
What is a Generative Pre-trained Transformer (GPT) model?
A Generative Pre-trained Transformer (GPT) is a type of artificial intelligence model that utilizes unsupervised learning to understand and generate human-like text. GPT models are pre-trained on vast amounts of text data and can be fine-tuned for specific tasks such as text generation, translation, summarization, and question-answering. In the context of machine learning, especially within
Is Python necessary for Machine Learning?
Python is a widely used programming language in the field of Machine Learning (ML) due to its simplicity, versatility, and the availability of numerous libraries and frameworks that support ML tasks. While it is not a requirement to use Python for ML, it is quite recommended and preferred by many practitioners and researchers in the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Does an unsupervised model need training although it has no labelled data?
An unsupervised model in machine learning does not require labeled data for training as it aims to find patterns and relationships within the data without predefined labels. Although unsupervised learning does not involve the use of labeled data, the model still needs to undergo a training process to learn the underlying structure of the data
What are some examples of semi-supervised learning?
Semi-supervised learning is a machine learning paradigm that falls between supervised learning (where all data is labeled) and unsupervised learning (where no data is labeled). In semi-supervised learning, the algorithm learns from a combination of a small amount of labeled data and a large amount of unlabeled data. This approach is particularly useful when obtaining
How does one know when to use supervised versus unsupervised training?
Supervised and unsupervised learning are two fundamental types of machine learning paradigms that serve distinct purposes based on the nature of the data and the objectives of the task at hand. Understanding when to use supervised training versus unsupervised training is crucial in designing effective machine learning models. The choice between these two approaches depends
How does one know if a model is properly trained? Is accuracy a key indicator and does it have to be above 90%?
Determining whether a machine learning model is properly trained is a critical aspect of the model development process. While accuracy is an important metric (or even a key metric) in evaluating the performance of a model, it is not the sole indicator of a well-trained model. Achieving an accuracy above 90% is not a universal
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning