Published inGoPenAI·PinnedCustom chatbot for websites using LangChain, Azure OpenAI API, FAISSTable of contents: Introduction Architecture Provide URLs Scrape data from the provided URLs Split data into chunks Get Embeddings Store Embeddings Query Part 3. Python Implementation Install required libraries Define OpenAI Azure API type, version, base, and key Define the OpenAI LLM model Give the list of URLs Split the…Large Language Models5 min readLarge Language Models5 min read

Published inGoPenAI·Apr 2, 2023The Future of Chatbots is here: Meet ChatGPT, the AI-Language Model that’s Changing the GameTable of Contents Introduction Large Language Models (LLMs) Transformer Encoder-Decoder architecture Generative Pre-trained Transformer (GPT) Reinforcement Learning ChatGPT training steps Why do we use decoders in GPT? But why are only decoders, and why aren’t only encoders, used in GPT? What is the prompt in ChatGPT? Python example to use…ChatGPT11 min readChatGPT11 min read

Jun 29, 2022AdaBoost Ensemble ModelTable of content: Introduction Three main ideas behind AdaBoost Steps of AdaBoost Reference End Notes Introduction: Adaptive Boosting or AdaBoost is a boosting ensemble technique that takes low variance and high bias models and uses the additive combination to reduce bias while keeping variance low. AdaBoost is most often used…Adaboost7 min readAdaboost7 min read

Jan 23, 2022K-Nearest Neighbor in Machine LearningTable of contents: Introduction Properties of KNN Steps of KNN algorithm Distance metrics in KNN KNN hyperparameter K How to select the value of K Data Preparation for KNN KNN for Classification KNN for Regression Pros of using KNN Cons of using KNN Applications of KNN References Introduction: K-Nearest Neighbor…Knn8 min readKnn8 min read

Jan 16, 2022Naive Bayes in Machine LearningTable of contents: Introduction Assumptions of Naive Bayes Bayes Theorem Example of Naive Bayes Problem with Naive Bayes Laplace or Additive Smoothing Bias Variance Tradeoff in Naive Bayes using hyper-parameter α Example of Laplace smoothing Applications of Naive Bayes Pros of using Naive Bayes Cons of using Naive Bayes How…Naive Bayes9 min readNaive Bayes9 min read

Jan 7, 2022Regularization in Machine LearningTable of contents: Overfitting Overfitting Examples How to overcome Overfitting? What parameters (θ’s) to penalize? Regularization Types of Regularization Regularization Parameter λ Why L1 creates Sparsity? References Overfitting: Sometimes the machine learning model performs well with the training data but does not perform well with the test data. It means…Regularization7 min readRegularization7 min read

Jan 3, 2022Logistic Regression-python implementation from scratch without using sklearnTable of contents: Generate data Split data into the train (75%) and test (25%) Standardize the data Initialize the weight_vector and intercept Compute Sigmoid Compute Log Loss Calculate Gradient w.r.t. ‘w’ Calculate Gradient w.r.t. ‘b’ Train the custom model Compare custom model with sklearn SGDClassifier model End Notes References In…Logistic Regression3 min readLogistic Regression3 min read

Jan 3, 2022Logistic RegressionTable of contents: Introduction Use of Linear Regression for classification issue Why Logistic Regression has term Regression in it The cost function for Logistic Regression Log Loss with example Log Loss cost function for Logistic Regression End Notes References Introduction: Logistic Regression is one of the most popular statistical models…Logistic Regression6 min readLogistic Regression6 min read

Dec 25, 2021Linear Regression on Boston House Pricing dataset using pythonTable of contents: Introduction Assumption in Linear Regression Hypothesis Cost Function Gradient descent Role of derivative term and learning rate Python example of Linear Regression using Boston House pricing dataset References In my previous article, I explained Linear Regression concepts. Please go through it if you want to know the…Linear Regression7 min readLinear Regression7 min read

Dec 10, 2021Linear RegressionTable of contents: Introduction Steps in Linear Regression Fitting a line to data using the least square method Calculate R² Calculate p-value for R² End Notes References Introduction: Linear Regression is a Supervised Machine learning model (where training data with the label is provided) which is used for Regression tasks…Linear Regression8 min readLinear Regression8 min read