Named Entity Recognition (NER) Named Entity Recognition (NER) Named Entity Recognition (NER) is a key task in Natural Language Processing (NLP) that identifies and classifies specific entities in text into predefined categories. 1. Definition NER = Extracting “names” or important entities from text and categorizing them. Input: Unstructured text Output: Labeled entities Common Entity Types: Person → Names of people Organization → Companies, institutions Location → Cities, countries, landmarks Date/Time → Dates, years, times Money → Amounts, currency Miscellaneous → Products, events, titles 2. Example Text: Apple Inc. was founded by Steve Jobs in Cupertino in 1976. NER Output: Entity Type Apple Inc. Organization Steve Jobs Person Cupertino Location 1976 Date ...

AI Data Flow & Risk of Wrong Real-Time Data AI Knowledge: Pre-Training vs Real-Time Data AI learns from historical data but can also fetch live data. Wrong real-time data can mislead AI answers. Offline Training Web pages, books, articles Curated datasets Code repositories Knowledge is fixed at training cut-off date. ➡️ Online Retrieval / Augmentation APIs Search engines / RAG Web browsing plugins Provides up-to-date information. If the real-time source is wrong, AI’s response may also be incorrect. ➡️ AI Answer Combines pre-trained knowledge and retrieved data Accuracy depends on both historical and real-time sources Wrong or misleading input data propagates to t...

IRACE in Machine Learning IRACE in Machine Learning IRACE stands for Iterated Racing for Automatic Algorithm Configuration . It is a method used to automatically tune hyperparameters of machine learning algorithms to achieve the best performance. 1. What is IRACE? Full Form: Iterated Racing for Automatic Algorithm Configuration Purpose: Optimize the hyperparameters of a machine learning algorithm automatically. Type: Hyperparameter optimization method using a racing-based strategy. 2. How IRACE Works The process is inspired by racing algorithms, which compare many configurations quickly and eliminate poor performers early. Steps: Generate Candidate Configurations: Randomly select a set of hyperparameter combinations. Evaluate Performance: Test each configuration on a dataset. Eliminate Poor Configurations: Remove candidates that perform badly early (like a “race”). Refine Candidates: Focus computational re...

  PyTorch Dataloader RuntimeError Explanation PyTorch RuntimeError: Stack expects each tensor to be equal size Error Message: RuntimeError: stack expects each tensor to be equal size, but got [1, 64, 64, 3] at entry 0 and [1, 64, 64] at entry 1 What Happened? This error occurs during DataLoader batch collation when PyTorch tries to stack multiple tensors into a single batch. In PyTorch, all tensors in a batch must have the same shape. Understanding the Tensor Shapes [1, 64, 64] : This represents a single-channel (grayscale) image with height and width 64 pixels. The first dimension ( 1 ) is the channel dimension. [1, 64, 64, 3] : This represents a multi-channel image (likely RGB) where the last dimension 3 represents 3 color channels (Red, Green, Blue). Since PyTorch expects tensors to have shape [channels, height, width] , a proper single-channel image should always have shape [1, 64, 64] . Having a 4D tensor with shape [1, 64, 64, ...

ROC-AUC Explanation ROC-AUC Explained ROC (Receiver Operating Characteristic) ROC is a curve/graph that shows how well your model separates classes. X-axis → False Positive Rate (FPR) Y-axis → True Positive Rate (TPR) It plots performance at different probability thresholds . AUC (Area Under Curve) AUC represents the area under the ROC curve . Its value ranges from 0 → 1 . AUC Score Meaning 1.0 Perfect model 0.9 – 0.99 Excellent 0.8 – 0.89 Good 0.7 – 0.79 Okay 0.5 Random guessing < 0.5 Worse than random ROC-AUC measures: How well your model ranks positive vs negative samples Higher AUC → better separation. Lower AUC → more confusion. Example Suppose: Cancer = Positive Healthy = Negative If the model gives higher probabilities to Cancer and lower to Healthy → AUC will be high (~0.9+). Formula TPR (Recall) = TP / (TP + FN) FPR = FP / (FP + TN) ROC plots TPR vs FPR . Why ROC-...

k-Space in MRI What is k-Space? k-space is the frequency domain representation of an image, mainly used in MRI (Magnetic Resonance Imaging) . Stores frequency and phase information Not directly viewable like a normal image Simple Definition k-space = raw data collected by MRI scanners before image reconstruction How it Works (Step-by-Step) MRI scanner collects signals from the body Data is stored in k-space (frequency domain) Apply inverse Fourier Transform Final image (spatial domain) is obtained Normal image → shows position (x, y) k-space → shows frequency components What Different Parts of k-Space Mean k-space Region Meaning Center Low frequency → overall shape and brightness Edges High frequency → edges and fine details An image is made of many waves. k-space stores those wave components. Important for Mac...

Normal Image vs DICOM Image For example, in MRI, data is acquired in the frequency domain (k-space) and then reconstructed into a spatial image (DICOM) using the Inverse Fourier Transform.  1. Normal Image (JPG, PNG, etc.) Examples: .jpg, .png, .jpeg What it contains: Only pixel values (visual image) Designed for display Key characteristics: Usually RGB (3 channels) Pixel values: typically 0–255 No medical meaning attached Lightweight and easy to use Example shape: [H, W, 3] → RGB image 2. DICOM Image (Medical Imaging) Example: .dcm DICOM = Digital Imaging and Communications in Medicine What it contains: Image (scan) Patient info (name, age, ID) Scan parameters (scanner type, resolution, modality) Metadata (very important) Key Differences Feature Normal Image DICOM Image Purpose Display Medical diagnosis Fo...

Microprocessor vs. Microcontroller Understanding the "Brain" vs. the "Complete System" 📘 Definitions 📊 Key Differences 💡 8086 vs. 8051 Microprocessor A microprocessor is a single-chip CPU (Central Processing Unit). It does not have built-in RAM, ROM, or I/O ports. To make it work, you must connect external components. Analogy: Like a high-performance engine that needs a separate fuel tank, wheels, and chassis to become a car. Microcontroller A microcontroller is a **"Computer on a Chip."** It contains a CPU, RAM, ROM, and I/O ports all integrated into one single integrated circuit (IC). Analogy: Like a small, pre-assembled toy car that comes with its own battery and motor ready to run. Core Comparison ...