AI

Introduction

Activation functions are a component of neural networks they introduce non-linearity into the model, enabling it to learn complex patterns. Without activation functions, a neural network would essentially act as a linear model, regardless of its depth.

Key Properties of Activation Functions

• Non-linearity: Enables the model to learn complex relationships.
• Differentiability: Allows backpropagation to optimize weights.
• Range: Defines the output range, impacting gradient flow.

In this post I will outline each of the most common activation functions how they are calculated and when they should be used.

Summary

In this post I will implement a Support Vector Machine (SVM) in python. Then describe what it does how it does it and some applications of the instrument.

What Are Support Vector Machines (SVM)?

Support Vector Machines (SVM) are supervised learning algorithms used for classification and regression tasks. Their strength lies in handling both linear and non-linear problems effectively. By finding the optimal hyperplane that separates classes, SVMs maximize the margin between data points of different classes, making them highly effective in high-dimensional spaces.

Summary

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances large language models (LLMs) by allowing them to use external knowledge sources.

An Artificial Intelligence (AI) system consists of components working together to apply knowledge learned from data. Some common components of those systems are:

• Large Language Model (LLM): Typically the core component of the system, often there is more than one. These are large models that have been trained on massive amounts of data and can make intelligent predictions based on their training.

Summary

CAG performs better but does not solve the key reason RAG was created small context windows.

Retrieval-Augmented Generation (RAG) is currently(early 2025) the most popular way to use external knowledge in current LLM opperations. RAG allows you to enhance your LLM with data beyond the data it was trained on. Ther are many great RAG solutions and products.

RAG has some drawbacks - There can be significant retreival latency as it searches for and organizes the correct data.
- There can be errors in the documents/data it selects as results for a query. For example it may select the wrong document or give priority to the wrong document. - It may interduce security and data issues 2️⃣.
- It introduces complication
- an external application to manage the data (Vector Database) - a process to continually update this data when the data goes stale

LLM Agents

Agents are used enhance and extend the functionality of LLM’s.

In this tutorial, we’ll explore what LLM agents are, how they work, and how to implement them in Python.

What Are LLM Agents?

An agent is an autonomous process that may use the LLM and other tools multiple times to achieve a goal. The LLM output often controls the workflow of the agent(s).

What is the difference between Agents and LLMs or AI?

Agents are processes that may use LLM’s and other agents to achieve a task. Agents act as orchestrators or facilitators, combining various tools and logic, whereas LLMs are the underlying generative engines.

Some FREE AI courses on Agents I recommend doing

Agents were important in Machine Learning development last year.

These are some courses I have done and recommend they are all free.
A good reason to do courses and look at youtube videos is you will learn current applications of AI and may get ideas for new applications.

AI Agentic Design Patterns with AutoGen

AI Agentic Design Patterns with AutoGen

Topics: Agents, Microsoft, Autogen.

Using Ollama

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Introduction

Ollama is the best platform for running, managing, and interacting with Large Language Models (LLM) models locally. For Python programmers, Ollama offers seamless integration and robust features for querying, manipulating, and deploying LLMs. In this post I will explore how Python developers can leverage Ollama for powerful and efficient AI-based workflows.

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1. What is Ollama?

Ollama is a tool designed to enable local hosting and interaction with LLMs. Unlike cloud-based APIs, Ollama prioritizes privacy and speed by running models directly on your machine. Key benefits include: