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
Why should we use i.e a KNN algorithm instead of an SVM algorithm and vice versa?
When evaluating whether to employ the k-Nearest Neighbors (KNN) algorithm or the Support Vector Machine (SVM) algorithm for a machine learning task, several critical aspects must be considered, including the theoretical underpinnings of each algorithm, their practical behavior under varying data conditions, computational complexity, interpretability, and the specific requirements of the application domain. Each algorithm
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
What is Quandl and how to currently install it and use it to demonstrate regression?
Quandl is a widely recognized platform that provides access to a broad array of financial, economic, and alternative datasets. It caters to professionals and researchers in data science, finance, economics, and related fields by offering a unified interface to both free and premium databases. Quandl's data is leveraged for tasks such as quantitative research, backtesting
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Regression, Introduction to regression
How important is TensorFlow for machine learning and AI and what are other major frameworks?
TensorFlow has played a significant role in the evolution and adoption of machine learning (ML) and artificial intelligence (AI) methodologies within both academic and industrial domains. Developed and open-sourced by Google Brain in 2015, TensorFlow was designed to facilitate the construction, training, and deployment of neural networks and other machine learning models at scale. Its
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Fundamentals of machine learning
How Keras models replace TensorFlow estimators?
The transition from TensorFlow Estimators to Keras models represents a significant evolution in the workflow and paradigm of machine learning model creation, training, and deployment, particularly within the TensorFlow and Google Cloud ecosystems. This change is not merely a shift in API preference but reflects broader trends in accessibility, flexibility, and the integration of modern
What are the main differences between classical and quantum neural networks?
Classical Neural Networks (CNNs) and Quantum Neural Networks (QNNs) represent two distinct paradigms in computational modeling, each grounded in fundamentally different physical substrates and mathematical frameworks. Understanding their differences requires an exploration of their architectures, computational principles, learning mechanisms, data representations, and the implications for implementing neural network layers, especially with respect to frameworks such
What was the exact problem solved in the quantum supremacy achievement?
Quantum supremacy is a milestone that refers to an experimental demonstration where a programmable quantum processor performs a well-defined computational task in a time that is infeasible for any known classical computer. The experiment reported by Google in 2019, carried out on the 53-qubit superconducting processor named “Sycamore”, is the first accepted demonstration of that
How to configure specific Python environment with Jupyter notebook?
Configuring a specific Python environment for use with Jupyter Notebook is a fundamental practice in data science, machine learning, and artificial intelligence workflows, particularly when leveraging Google Cloud Machine Learning (AI Platform) resources. This process ensures reproducibility, dependency management, and isolation of project environments. The following comprehensive guide addresses the configuration steps, rationale, and best
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Working with Jupyter
How to use TensorFlow Serving?
TensorFlow Serving is an open-source system developed by Google for serving machine learning models, particularly those built using TensorFlow, in production environments. Its primary purpose is to provide a flexible, high-performance serving system for deploying new algorithms and experiments while maintaining the same server architecture and APIs. This framework is widely adopted for model deployment
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators