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
What is one hot encoding?
One hot encoding is a technique used in machine learning and data processing to represent categorical variables as binary vectors. It is particularly useful when working with algorithms that cannot handle categorical data directly, such as plain and simple estimators. In this answer, we will explore the concept of one hot encoding, its purpose, and
How to install TensorFlow?
TensorFlow is a popular open-source library for machine learning. To install it you first need to install Python. Please be advised that the exemplary Python and TensorFlow instructions serve only as an abstract reference to plain and simple estimators. Detailed instructions on using TensorFlow 2.x version will follow in subsequent materials. If you would like
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
Can deep learning be interpreted as defining and training a model based on a deep neural network (DNN)?
Deep learning can indeed be interpreted as defining and training a model based on a deep neural network (DNN). Deep learning is a subfield of machine learning that focuses on training artificial neural networks with multiple layers, also known as deep neural networks. These networks are designed to learn hierarchical representations of data, enabling them
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
Is it correct to call a process of updating w and b parameters a training step of machine learning?
A training step in the context of machine learning refers to the process of updating the parameters, specifically the weights (w) and biases (b), of a model during the training phase. These parameters are crucial as they determine the behavior and effectiveness of the model in making predictions. Therefore, it is indeed correct to state
Does Google’s TensorFlow framework enable to increase the level of abstraction in development of machine learning models (e.g. with replacing coding with configuration)?
The Google TensorFlow framework indeed enables developers to increase the level of abstraction in the development of machine learning models, allowing for the replacement of coding with configuration. This feature provides a significant advantage in terms of productivity and ease of use, as it simplifies the process of building and deploying machine learning models. One
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators