Are advanced searching capabilities a Machine Learning use case?
Advanced searching capabilities are indeed a prominent use case of Machine Learning (ML). Machine Learning algorithms are designed to identify patterns and relationships within data to make predictions or decisions without being explicitly programmed. In the context of advanced searching capabilities, Machine Learning can significantly enhance the search experience by providing more relevant and accurate
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
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 the types of hyperparameter tuning?
Hyperparameter tuning is a crucial step in the machine learning process as it involves finding the optimal values for the hyperparameters of a model. Hyperparameters are parameters that are not learned from the data, but rather set by the user before training the model. They control the behavior of the learning algorithm and can significantly
What are some examples of hyperparameter tuning?
Hyperparameter tuning is a crucial step in the process of building and optimizing machine learning models. It involves adjusting the parameters that are not learned by the model itself, but rather set by the user prior to training. These parameters significantly impact the performance and behavior of the model, and finding the optimal values for
Is it correct that initial dataset can be spit into three main subsets: the training set, the validation set (to fine-tune parameters), and the testing set (checking performance on unseen data)?
It is indeed correct that the initial dataset in machine learning can be divided into three main subsets: the training set, the validation set, and the testing set. These subsets serve specific purposes in the machine learning workflow and play a crucial role in developing and evaluating models. The training set is the largest subset
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
How ML tuning parameters and hyperparameters are related to each other?
Tuning parameters and hyperparameters are related concepts in the field of machine learning. Tuning parameters are specific to a particular machine learning algorithm and are used to control the behavior of the algorithm during training. On the other hand, hyperparameters are parameters that are not learned from the data but are set prior to the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
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
Which ML algorithm is suitable to train model for data document comparison?
One algorithm that is well suited to train a model for data document comparison is the cosine similarity algorithm. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. In the context of document comparison, it is used to determine
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
What are large linguistic models?
Large linguistic models are a significant development in the field of Artificial Intelligence (AI) and have gained prominence in various applications, including natural language processing (NLP) and machine translation. These models are designed to understand and generate human-like text by leveraging vast amounts of training data and advanced machine learning techniques. In this response, we
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