Downsampling techniques can be used to create custom batches for training a machine learning model. These custom batches can be tailored to specific needs, such as improving training efficiency, handling imbalanced data, or focusing on… Downsampling Techniques for Custom Batches in training Machine Learning Models
Downsampling for hyperparameter tuning reduces the dataset size to speed up model training and experimentation while preserving key data characteristics. Here’s a concise overview: Why Downsample for Hyperparameter Tuning? Key Considerations Practical Steps Pitfalls to… downsampling for hyperparameter tuning
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
The central idea of the paper “Textbooks Are All You Need” is that data quality is significantly more important than data quantity or model size when training large language models (LLMs), particularly for code generation.… Textbooks Are All You Need
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
The major conclusion of the paper Similarity Metrics for MR Image-to-Image Translation is that relying on the most commonly used metrics, specifically SSIM and PSNR, is insufficient for validating Magnetic Resonance (MR) image-to-image translation models… Similarity Metrics for MR Image-to-Image Translation
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
Explainable AI (XAI) techniques are methods and processes used to make AI models and their predictions understandable to humans. These techniques are critical for building trust, ensuring ethical use, and meeting regulatory requirements in AI… An overview of explainable AI techniques
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
The paper “Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer” by Sergey Zagoruyko, Nikos Komodakis Université proposed a novel training methodology called attention transfer to improve the performance of convolutional neural… Attention Transfer for Convolutional Neural Networks
The method described in the paper “Investigating molecular transport in the human brain from MRI with physics‑informed neural networks” by Bastian Zapf, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, Kent‑Andre Mardal centres on… Physics-Informed Neural Networks for Brain Molecular Transport from MRI
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
The paper “ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation,” from the KDD ’24 conference, addresses the pervasive issue of missing data in spatiotemporal datasets, particularly contrasting traditional low-rank models with modern deep learning methods like… ImputeFormer: Low Rankness-Induced Transformers forGeneralizable Spatiotemporal Imputation
The paper Imputation Using Training Labels and Classification via Label Imputation introduces two novel machine learning algorithms designed to efficiently handle missing values, a common issue in practical datasets. The first approach, Classification Based on… Classification via Label Imputation and Imputation Using Labels
In this post, we will talk about feature-based knowledge distillation. One of the pioneering paper is “FitNets: Hints for Thin Deep Nets” by Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang,Carlo Gatta & Yoshua… Feature-Based Knowledge Distillation in Pytorch on MNIST
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
Hierarchical classification is a method of assigning items to a category that is part of a larger, structured hierarchy. Unlike traditional “flat” classification where categories are independent, hierarchical classification considers the relationships between categories, organizing… What is Hierarchical Classification + Python Code
