Does a machine learning model need supevision during its training?
The process of training a machine learning model involves exposing it to vast amounts of data to enable it to learn patterns and make predictions or decisions without being explicitly programmed for each scenario. During the training phase, the machine learning model undergoes a series of iterations where it adjusts its internal parameters to minimize
What is classifier?
A classifier in the context of machine learning is a model that is trained to predict the category or class of a given input data point. It is a fundamental concept in supervised learning, where the algorithm learns from labeled training data to make predictions on unseen data. Classifiers are extensively used in various applications
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
What is machine learning?
Machine learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It is a powerful tool that allows machines to automatically analyze and interpret complex data, identify patterns, and make informed decisions or predictions.
What is a labeled data?
A labeled data, in the context of Artificial Intelligence (AI) and specifically in the domain of Google Cloud Machine Learning, refers to a dataset that has been annotated or marked with specific labels or categories. These labels serve as the ground truth or reference for training machine learning algorithms. By associating data points with their
Can machine learning predict or determine the quality of the data used?
Machine Learning, a subfield of Artificial Intelligence, has the capability to predict or determine the quality of the data used. This is achieved through various techniques and algorithms that enable machines to learn from the data and make informed predictions or assessments. In the context of Google Cloud Machine Learning, these techniques are applied to
What are the distinctions between supervised, unsupervised and reinforcement learning approaches?
Supervised, unsupervised, and reinforcement learning are three distinct approaches in the field of machine learning. Each approach utilizes different techniques and algorithms to address different types of problems and achieve specific objectives. Let’s explore the distinctions between these approaches and provide a comprehensive explanation of their characteristics and applications. Supervised learning is a type of
What is ML?
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. ML algorithms are designed to analyze and interpret complex patterns and relationships in data, and then use this knowledge to make informed
What is a general algorithm for defining a problem in ML?
Defining a problem in machine learning (ML) involves a systematic approach to formulating the task at hand in a way that can be addressed using ML techniques. This process is crucial as it lays the foundation for the entire ML pipeline, from data collection to model training and evaluation. In this answer, we will outline
What is the purpose of generating training samples in the context of training a neural network to play a game?
The purpose of generating training samples in the context of training a neural network to play a game is to provide the network with a diverse and representative set of examples that it can learn from. Training samples, also known as training data or training examples, are essential for teaching a neural network how to
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Training a neural network to play a game with TensorFlow and Open AI, Training data, Examination review