AI Practitioner

Learn about z-test and p-value in statistics with detailed examples and Python code. Understand how they apply to Machine Learning and Deep Learning for model evaluation. What is a P-Value? The p-value is a probability that measures the strength of the evidence against the null hypothesis. Specifically, it is the probability of observing a test statistic (like the z-score) at least as extreme as the one computed from your sample, assuming that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis. Common thresholds to reject the null hypothesis are: p < 0.05: statistically significant p < 0.01: highly significant Python Example of Z-Test Let’s assume we want to test whether the mean of a sample differs from a known population mean: import numpy as np from scipy import stats # Sample data sample = [2.9, 3.0, 2.5, 3.2, 3.8, 3.5] mu = 3.0 # Population mean sigma = 0.5 # Population std dev...

Relational Deep Learning (RDL)  proposes a unified graph-based way to model multi-table databases for end-to-end learning using GNNs. This retains relational semantics, avoids joins, and supports temporal reasoning. It’s a paradigm shift that bridges the gap between ML and databases. 1. Motivation: From Tables to Graphs Traditional Setup Relational databases store structured data across multiple normalized tables , each capturing different types of entities (e.g., users, orders, products). These tables are linked by foreign-key (FK) and primary-key (PK) constraints. To train machine learning models, these databases are typically flattened into a single table using joins , and domain experts manually select and engineer features. Problems: Joins are expensive and brittle (schema changes break pipelines). Manual feature engineering is time-consuming and lacks relational awareness. Loss of information about cross-entity relationships . 2. Core Idea: Learn Direc...

Understanding the XGBoost Algorithm with Detailed Explanation and Python Implementation XGBoost, short for "Extreme Gradient Boosting", is a powerful algorithm widely used in machine learning, especially for regression and classification problems. It is known for delivering high performance and is frequently used in Kaggle competitions. In this article, we’ll explore XGBoost’s key features, a basic Python implementation, and a practical example using the Scikit-learn library. Key Features of XGBoost Boosting: Combines multiple weak learners (typically decision trees) sequentially to create a strong learner. Each tree corrects the errors of the previous one. Gradient Boosting: Adds trees based on the gradient of the loss function, optimizing using gradient descent. Regularization: Applies L1 and L2 regularization to control model complexity and prevent overfitting. Tree Pruning: Uses max depth pruning to reduce unnecessary complexity. Handling Missing Values: Aut...

How to Save and Retrieve a Vector Database using LangChain, FAISS, and Gemini Embeddings Efficient storage and retrieval of vector databases is foundational for building intelligent retrieval-augmented generation (RAG) systems using large language models (LLMs). In this guide, we’ll walk through a professional-grade Python implementation that utilizes LangChain with FAISS and Google Gemini Embeddings to store document embeddings and retrieve similar information. This setup is highly suitable for advanced machine learning (ML) and deep learning (DL) engineers who work with semantic search and retrieval pipelines. Why Vector Databases Matter in LLM Applications Traditional keyword-based search systems fall short when it comes to understanding semantic meaning. Vector databases store high-dimensional embeddings of text data, allowing for approximate nearest-neighbor (ANN) searches based on semantic similarity. These capabilities are critical in applications like: Question Ans...

Vector Database (Vector DB): A Deep Dive for ML/DL Engineers What is a Vector Database? A Vector Database (Vector DB) is a specialized type of database designed to efficiently store, index, and query high-dimensional vectors. These vectors often represent embeddings from deep learning models—semantic representations of data such as text, images, audio, or code. Unlike traditional relational databases that rely on exact key-based lookups or structured queries, vector databases are optimized for approximate or exact nearest neighbor (ANN or NNS) searches, which are fundamental to tasks such as semantic search, recommendation systems, anomaly detection, and generative AI retrieval-augmented generation (RAG). Core Components of a Vector Database A production-grade vector database typically comprises the following components: Embedding Store: A storage engine for high-dimensional vectors with metadata. Indexing Engine: Structures like HNSW, IVF, PQ, or ANNOY to support f...

What is a Transformer? The Transformer is a neural network architecture introduced by Vaswani et al. in the 2017 paper "Attention is All You Need." It revolutionized natural language processing by replacing sequential models like RNNs and LSTMs. Transformers process entire sentences in parallel using self-attention , effectively addressing the difficulty of learning from long input sequences and enabling high computational efficiency by overcoming the limitations of sequential processing. 1. Transformer Components and Overcoming RNN/LSTM Limitations The Transformer is composed of an encoder and a decoder, with each block consisting of the following key components: Self-Attention: Learns the relationships between tokens within the input sequence by enabling each token to attend to all others, effectively capturing long-range dependencies and rich contextual information. Multi-Head Attention (MHA): Divides self-attention into multiple parallel heads. Each head focuses o...

Singular Value Decomposition (SVD) is a matrix factorization method widely used in mathematics, engineering, and economics. Since it's a crucial concept applied in accelerating matrix computations and data compression, it's worth studying at least once. SVD (Singular Value Decomposition) Theory Singular Value Decomposition (SVD) is a matrix factorization technique applicable to any real or complex matrix. Any matrix  A (m×n)  can be decomposed as follows: $A = U * \Sigma * V^T$ U : Orthogonal matrix composed of left singular vectors $(m \times m)$ Σ : Diagonal matrix $(m \times n)$ with singular values on the diagonal $V^T$ : Transposed matrix of right singular vectors $(n \times n)$ The singular values represent the energy or information content of matrix A, enabling tasks like dimensionality reduction or noise filtering. Truncated SVD Truncated SVD approximates the original matrix using only the top  k  singular values and corresponding singular vectors: $A \approx...

Paper Link: https://arxiv.org/pdf/2001.07685 What Problem Does FixMatch Address? FixMatch is a semi-supervised learning (SSL) algorithm designed to solve two long-standing technical challenges using a unified and simple framework. In many real-world machine learning applications, labeled data is expensive and time-consuming to obtain, while unlabeled data is abundant. FixMatch addresses this imbalance by combining two powerful ideas in SSL: Consistency Regularization: The assumption that a model should produce consistent predictions when the input undergoes small augmentations or perturbations. Pseudo-Labeling: Treating high-confidence predictions on unlabeled data as if they were ground truth labels for training purposes. While previous SSL methods often combined these ideas through complex architectures or training pipelines, FixMatch simplifies the process using a confidence threshold and a two-stage data augmentation strategy to achieve state-of-the-art performance ...

  What is KL Divergence? Kullback–Leibler Divergence (KL Divergence)  is a fundamental concept in probability theory, information theory, and machine learning. It measures the difference between two probability distributions. In essence,  KL Divergence tells us how much information is lost  when we use one distribution ( Q ) to approximate another distribution ( P ). It’s often described as a measure of "distance" between distributions — but  important : it is  not a true distance  because it is  not symmetric . That means: $KL(P \parallel Q) \neq KL(Q \parallel P)$ Why is KL Divergence Important in Deep Learning? KL Divergence shows up in many core ML/DL areas: Variational Autoencoders (VAE) : Regularizes the latent space by minimizing KL divergence between the encoder's distribution and a prior (usually standard normal). Language Models : Loss functions like  cross-entropy  are tightly related to KL Divergence. Reinforcement Learning :...