AI

Summary Searching through massive datasets efficiently is a challenge, whether in image retrieval, recommendation systems, or semantic search. Faiss (Facebook AI Similarity Search) is a powerful open-source library developed by Meta to handle high-dimensional similarity search at scale. It’s particularly well-suited for tasks like: Image search: Finding visually similar images in a large database. Recommendation systems: Recommending items (products, movies, etc.) to users based on their preferences. Semantic search: Finding documents or text passages that are semantically similar to a given query.

Summary Imagine you have a dataset of customer profiles. How can you group similar customers together to tailor marketing campaigns? This is where K-Means clustering comes into play. K-Means is a popular unsupervised learning algorithm used for clustering data points into distinct groups based on their similarities. It is widely used in various domains such as customer segmentation, image compression, and anomaly detection. In this blog post, we’ll cover how K-Means works and demonstrate its implementation in Python using scikit-learn.

Summary Large Language Models (LLMs) are powerful, but their size can lead to slow inference speeds and high memory consumption, hindering real-world deployment. Quantization, a technique that reduces the precision of model weights, offers a powerful solution. This post will explore how to use quantization techniques like bitsandbytes, AutoGPTQ, and AutoRound to dramatically improve LLM inference performance. What is Quantization? Quantization reduces the computational and storage demands of a model by representing its weights with lower-precision data types.

Summary This post kicks off a series of three where we’ll build, extend, and use the open-source DeepResearch application inspired by the Hugging Face blog post. In this first part, we’ll focus on creating an arXiv search tool that can be used with SmolAgents. DeepResearch aims to empower research by providing tools that automate and streamline the process of discovering and managing academic papers. This series will demonstrate how to build such tools, starting with a powerful arXiv search tool.

Summary This post provides a practical guide to building common neural network architectures using PyTorch. We’ll explore feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, transformers, autoencoders, and GANs, along with code examples and explanations. 1. Understanding PyTorch’s Neural Network Module PyTorch provides the torch.nn module to build neural networks. It provides classes for defining layers, activation functions, and loss functions, making it easy to create and manage complex network architectures in a structured way.

Summary This post provides a comprehensive guide to prompt engineering, the art of crafting effective inputs for Large Language Models (LLMs). Mastering prompt engineering is crucial for maximizing the potential of LLMs and achieving desired results. Effective prompting is the easiest way to enhance your experience with Large Language Models (LLMs). The prompts we make are our interface to LLMs. This is how we communicate with them. This is why it is important to understand how to do it well.

Summary In this blog I aim to try building using open source tools where possible. The benefits are price, control, knowledge and eventually quality. In the shorter term though the quality will trail the paid versions. My belief is we can construct AI applications to be self correcting sort of like how your camera auto focuses for you. This process will involve a lot of computation so using a paid service could be costly.

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.

44. What does it mean to Fit a Model? Answer Fitting a model refers to the process of adjusting the model’s internal parameters to best match the given training data. It’s like tailoring a suit – you adjust the fabric and stitching to make it fit the wearer perfectly. Key Terms: Model: A mathematical representation that captures patterns in data. Examples include linear regression, decision trees, neural networks, etc. Parameters: These are the internal variables within the model that determine its behavior.

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.

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?

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 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. 1. What is Ollama? Ollama is a tool designed to enable local hosting and interaction with LLMs.