Deep Learning with Yacine on MSN

Adadelta Optimizer From Scratch in Python – Step-by-Step Tutorial

Learn how to implement the Adadelta optimization algorithm from scratch in Python. This tutorial explains the math behind ...
IEEE

Towards Fast Gaussian Process Regression Models: An FPGA-based Implementation of the RBF Kernel Matrix Computation

Abstract: Gaussian Process Regression (GPR) is a machine learning technique that, besides predicting certain target values, also quantifies their uncertainty. With that, GPR is increasingly gaining ...
GitHub

.Net: New Feature: Agent Memory; AIContextProvider implementation for Python

Related to #10562 that is stale and #10100 Agent Memory for C#. I would like to propose contributing the Agent Memory: AIContextProvider implementation for Python. I would also propose contributing ...
Hosted on MSN

Adagrad Algorithm Explained — Python Implementation from Scratch

Learn how the Adagrad optimization algorithm works and see how to implement it step by step in pure Python — perfect for beginners in machine learning! #Adagrad #MachineLearning #PythonCoding FBI ...
GitHub

Python: What is the alternative to Kernel.Data in C# within python implementation

I want to pass some additional context to my semantic kernel plugins for each request. Scenario: I currently have different plugins for retrieving search documents or images from my vector index. For ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict a person's bank savings account balance based on their age, years of ...