Catastrophic forgetting occurs when a neural network “overwrites” what it learned in a previous task while training on a new one. This happens because the weights optimized for the first task are changed to minimize… How to reduce catastrophic forgetting when fine-tuning neural networks
Cache Augmented Generation (CAG) is an architecture for Large Language Models (LLMs) that removes the need for real-time data retrieval by pre-loading a knowledge base directly into the model’s active memory. In practical terms, while… What’s Cache Augmented Generation (CAG)
📐 Definitions (for clarity) Let errors be . MAE MSE RMSE 📐 Relationship Between and Let the errors be Then: Mean Absolute Error (MAE) Mean Squared Error (MSE) Key Relationship 1. Jensen’s Inequality gives: Why?… Relationship Between MAE, MSE, RMSE
Polynomial regression is a form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an degree polynomial. Polynomial regression fits a nonlinear relationship between the value of… Polynomial regression in Python
Contrastive learning is a technique used in machine learning, particularly in the field of self-supervised and unsupervised learning. It focuses on learning to distinguish between similar and dissimilar pairs of data points by contrasting them… Why is contrastive learning?
Choosing a boyfriend as a maximum likelihood problem can be framed as an exercise in probabilistic decision-making, where the goal is to maximize the likelihood of selecting a partner who best fits your desired criteria… Choosing a Boyfriend Explained: Dating as a Maximum Likelihood Problem with the EM Algorithm
Common distance measures in machine learning, their formulas, use cases, and detailed properties: 1. Euclidean Distance 2. Manhattan Distance (L1 Norm) 3. Minkowski Distance 4. Cosine Similarity 5. Hamming Distance 6. Jaccard Distance 7. Mahalanobis… Common distance measures in machine learning and their properties
Finding datasets that naturally feature missing sequences (where patients were simply not scanned with the full protocol) is rare because public repositories usually curate data to ensure completeness. However, several high-profile datasets are specifically designed… brain datasets that have missing mri sequences
The Expectation-Maximization (EM) algorithm is an iterative approach to estimate the parameters of probabilistic models, such as a Gaussian (normal) distribution, when the data is incomplete or has missing values. It alternates between two steps:… EM algorithm
This example demonstrates Knowledge Distillation, a technique where a small “student” model is trained to mimic a larger, pre-trained “teacher” model. Let’s have a brief introduction to Knowledge Distillation first. 🎓 What is Knowledge Distillation?… Example of Knowledge Distillation (KD) using PyTorch on the MNIST dataset
Knowledge distillation and perceptual loss are distinct concepts in machine learning, but they can be used together effectively, especially in computer vision tasks. Here’s the simple breakdown: 🧠 What is Knowledge Distillation? Knowledge Distillation is… relation between knowledge distill and perceptual loss
Visual Studio Code is the most popular and recommended code editor for Rust development. The experience is excellent, but it requires one key extension to make it work. How to Set It Up (2 Steps)… Using Rust with Visual Studio Code
What is RUST? Rust is a modern systems programming language focused on three core goals: performance, memory safety, and concurrency. Think of it as having the raw speed and low-level control of languages like C… RUST for AI software development
The simplest way to think about it is: Think of an MRI as a high-resolution photograph of the brain, showing you exactly what it looks like. An fMRI is more like a video or a… Difference between MRI and fMRI
The application of artificial intelligence (AI) in healthcare, particularly in medical imaging, holds immense promise for improving the detection, diagnosis, and treatment of human diseases. However, the translation of AI models from research prototypes to… MONAI Framework for Advanced Brain Image Analysis
Ice melts faster on metal because metal is an excellent thermal conductor. This means it quickly transfers heat from the surrounding environment (like air or a tabletop) into the ice. Materials like wood, plastic, or… Why Ice Melts Faster on Metal
Here are some more examples of MCAR (recall that Missing completely at random (MCAR) data occurs when the probability of missing data on a variable is independent of any other measured variables and the underlying… What’s missing completely at random data
Speech-to-text technology, also known as automatic speech recognition (ASR), converts spoken language into written text. At its core, this technology analyzes sound waves and transforms them into their corresponding text equivalents. This process involves sophisticated… How Speech-to-text technology works
An SDF, or Signed Distance Function (sometimes called a Signed Distance Field), is a mathematical way to represent a shape by measuring the distance from any point in space to the closest point on that… Signed Distance Function (SDF)
In AI image generation, the ability to guide the creative process with precision is paramount. While text prompts provide a general direction, advanced techniques are needed for fine-grained control over elements like character poses, composition,… Guided AI Image Generation: ControlNet and Conditional Flow Matching
“Do you know what segmentation is?” Jon asked the magician. The magician replied: “It’s the process of dividing an image into meaningful regions” “like giving every pixel a role. So, you know the pixels that… Segmentation: definition & types
Blaze was fascinated by the tiny details of the world below. He dreamt of a way to instantly recognize every flower, every rock, and every scurrying critter in the meadow when he flies. One day,… Fast R-CNN
“Most neural networks pick one filter size at a time. But why not do all of them at once?” Inception said. Give me a photo, I can zoom in on tiny details, look at medium-sized… Inception: The Neural Network That Thinks in Parallel
Here, we will use the LangGraph library to create a simple AI agent that can decide whether to answer a user’s question directly or use a “search” tool to find the answer first. The system… Example of LangGraph with Ollama for conditional logic
LangGraph is a powerful, open-source framework for building and managing complex, stateful, and long-running AI agents. It provides a flexible and controllable way to create sophisticated AI workflows by representing them as graphs. At its… What’s LangGraph
Perceptual loss is a type of loss function used in AI, especially for tasks like creating or changing images. Instead of comparing two images pixel by pixel, it measures the difference between them based on… Perceptual loss
Developed by João Moura, crewAI provides a structured environment for orchestrating autonomous AI agents, enabling them to collaborate and tackle complex tasks that would be challenging for a single AI model to handle alone. At… crewAI: Orchestrating Collaborative AI Agents for Complex Task: example in Ollama
This is a common dependency conflict. So, sometimes, when you successfully install crewAI, you may encounter problems when using it. To resolve this, you need to install the specific version of litellm that crewai requires.… Fixed: crewai 0.186.1 requires litellm==1.74.9, but you have litellm 1.77.1 which is incompatible.
Running large language models locally on a laptop is becoming increasingly feasible, and Ollama makes it accessible. The key to a good experience is choosing a model that matches your laptop’s hardware, primarily its RAM… Ollama models that can be run on a laptop
