Is the so called part of "Inference" equivalent to the description in the step-by-step process of machine learning described as "evaluating, iterating, improving"?
The terms "Inference" and the process described as "evaluating, iterating, improving" are both fundamental to machine learning, yet they denote distinctly different phases within the broader workflow of developing and deploying machine learning models, particularly in the context of Google Cloud Machine Learning and similar frameworks. Definition of Inference in Machine Learning Inference in machine
What are some common AI/ML algorithms to be used on the processed data?
In the context of Artificial Intelligence (AI) and Google Cloud Machine Learning, the processed data—meaning data that has undergone cleaning, normalization, feature extraction, and transformation—is ready for machine learning algorithms to learn patterns, make predictions, or classify information. The selection of a suitable algorithm is driven by the underlying problem, the structure and type of
Can Machine Learning adapt which algorithm to use depending on a scenario?
Machine learning (ML) is a discipline within artificial intelligence that focuses on building systems capable of learning from data and improving their performance over time without being explicitly programmed for each task. A central aspect of machine learning is algorithm selection: choosing which learning algorithm to use for a particular problem or scenario. This selection
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How does an already trained machine learning model takes new scope of data into account?
When a machine learning model is already trained and encounters new data, the process of integrating this new scope of data can take several forms, depending on the specific requirements and context of the application. The primary methods to incorporate new data into a pre-trained model include retraining, fine-tuning, and incremental learning. Each of these
How to limit bias and discrimination in machine learning models?
To effectively limit bias and discrimination in machine learning models, it is essential to adopt a multi-faceted approach that encompasses the entire machine learning lifecycle, from data collection to model deployment and monitoring. Bias in machine learning can arise from various sources, including biased data, model assumptions, and the algorithms themselves. Addressing these biases requires
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
How to protect the privacy of data used to train machine learning models?
Protecting the privacy of data used to train machine learning models is a critical aspect of responsible AI development. It involves a combination of techniques and practices designed to ensure that sensitive information is not exposed or misused. This task has become increasingly important as the scale and complexity of machine learning models grow, and
How to ensure transparency and understandability of decisions made by machine learning models?
Ensuring transparency and understandability in machine learning models is a multifaceted challenge that involves both technical and ethical considerations. As machine learning models are increasingly deployed in critical areas such as healthcare, finance, and law enforcement, the need for clarity in their decision-making processes becomes paramount. This requirement for transparency is driven by the necessity
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Where is the information about a neural network model stored (including parameters and hyperparameters)?
In the domain of artificial intelligence, particularly concerning neural networks, understanding where information is stored is important for both model development and deployment. A neural network model consists of several components, each of which plays a distinct role in its operation and efficacy. Two of the most significant elements within this framework are the model's
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the difference between machine learning in computer vision and machine learning in LLM?
Machine learning, a subset of artificial intelligence, has been applied to various domains, including computer vision and language learning models (LLMs). Each of these fields leverages machine learning techniques to solve domain-specific problems, but they differ significantly in terms of data types, model architectures, and applications. Understanding these differences is essential to appreciate the unique
How essential is Python or other programming language knowledge to implement ML in practice?
To address the question of how necessary Python or any other programming language knowledge is for implementing machine learning (ML) in practice, it is vital to understand the role programming plays in the broader context of machine learning and artificial intelligence (AI). Machine learning, a subset of AI, involves the development of algorithms that allow
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning