Machine learning Archives - GaussianWaves

Shrink to Win: Regularization’s Role in Model Success

Mathuranathan — Sat, 01 Mar 2025 08:11:48 +0000

Overfitting? Shrink your model! Regularization prevents memorization, enabling true learning & robust success. Discover how. A central problem in machine learning is to develop algorithms that work well both on training data and on new inputs (test data). Most machine learning tasks can be generalized as the estimation of a function \(\hat{f}(X)\) that maps the ... Read more

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Model Distillation Explained: How DeepSeek Leverages the Technique for AI Success

Mathuranathan — Wed, 26 Feb 2025 08:45:06 +0000

Model distillation, also known as knowledge distillation, is a supervised learning technique that condenses the capabilities and thought processes of a large, pre-trained “teacher” model into a smaller “student” model. This allows the student model to achieve comparable performance to the teacher model, but at a lower cost and with faster performance. Chinese AI lab ... Read more

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Machine Learning Basics: A Comprehensive Introduction for Beginners

Mathuranathan — Mon, 21 Mar 2022 14:02:00 +0000

Key focus: machine learning, introduction, basics, beginners, algorithms, applications, concepts Introduction Machine learning has emerged as a groundbreaking technology that is transforming industries and reshaping our interaction with technology. From personalized recommendations to autonomous vehicles, machine learning algorithms play a pivotal role in these advancements. If you’re new to the field, this comprehensive beginner’s guide ... Read more

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Implementing Markov Chain in Python

Mathuranathan — Sun, 20 Mar 2022 15:30:00 +0000

Keywords: Markov Chain, Python, probability, data analysis, data science Markov Chain Markov chain is a probabilistic models that describe a sequence of observations whose occurrence are statistically dependent only on the previous ones. This article is about implementing Markov chain in Python Markov chain is described in one of the earlier posts. For better understanding ... Read more

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The Most Important Topics to Learn in Machine Learning

Mathuranathan — Thu, 17 Mar 2022 15:30:00 +0000

Keywords: machine learning, topics, probability, statistics, linear algebra, data preprocessing, supervised learning, unsupervised learning, deep learning, reinforcement learning, model evaluation, cross-validation, hyperparameter tuning. Why the buzz ? Machine learning has been generating a lot of buzz in recent years due to its ability to automate tasks that were previously thought to be impossible or required ... Read more

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Hidden Markov Models (HMM) – Simplified !!!

Mathuranathan — Tue, 17 Mar 2020 05:08:35 +0000

Markov chains are useful in computing the probability of events that are observable. However, in many real world applications, the events that we are interested in are usually hidden, that is we don’t observe them directly. These hidden events need to be inferred. For example, given a sentence in a natural language we only observe the ... Read more

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Markov Chains – Simplified !!

Mathuranathan — Fri, 06 Mar 2020 07:11:37 +0000

Key focus: Markov chains are a probabilistic models that describe a sequence of observations whose occurrence are statistically dependent only on the previous ones. ● Time-series data like speech, stock price movements.● Words in a sentence.● Base pairs on the rung of a DNA ladder. States and transitions Assume that we want to model the ... Read more

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Linear regression using python – demystified

Mathuranathan — Wed, 05 Feb 2020 06:52:00 +0000

Key focus: Let’s demonstrate basics of univariate linear regression using Python SciPy functions. Train the model and use it for predictions. Linear regression model Regression is a framework for fitting models to data. At a fundamental level, a linear regression model assumes linear relationship between input variables () and the output variable (). The input ... Read more

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Generating simulated dataset for regression problems

Mathuranathan — Fri, 31 Jan 2020 03:00:12 +0000

Key focus: Generating simulated dataset for regression problems using sklearn make_regression function (Python 3) is discussed in this article. Problem statement Suppose, a survey is conducted among the employees of a company. In that survey, the salary and the years of experience of the employees are collected. The aim of this data collection is to ... Read more

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Introduction to Signal Processing for Machine Learning

Mathuranathan — Tue, 21 Jan 2020 07:55:26 +0000

Key focus: Fundamentals of signal processing for machine learning. Speaker identification is taken as an example for introducing supervised learning concepts. Signal Processing A signal, mathematically a function, is a mechanism for conveying information. Audio, image, electrocardiograph (ECG) signal, radar signals, stock price movements, electrical current/voltages etc.., are some of the examples. Signal processing is ... Read more

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