ML Journey Posts – BendThe-Curve

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- Note: Advanced posts with heavy computations are currently under maintenance

Welcome to BendTheCurve

welcome

introduction

Your friendly guide to the exciting world of data science and machine learning

Advanced Neural Network Architectures: A Technical Deep Dive

deep-learning

neural-networks

architectures

mathematics

A comprehensive technical exploration of advanced neural network architectures, including transformers, attention mechanisms, and modern architectural patterns.

Algorithmic Stability and Learning Theory

machine-learning

theory

mathematics

stability

A beginner-friendly guide to algorithmic stability in machine learning, with interactive visualizations and practical examples.

Causal Inference and Structural Learning

machine-learning

causality

statistics

mathematics

A rigorous exploration of causal inference and structural learning, covering identification, estimation, and structural causal models.

Computational Learning Theory

machine-learning

theory

complexity

algorithms

A beginner-friendly guide to computational learning theory with interactive visualizations and practical examples.

Deep Learning: The Technology Behind AI’s Recent Breakthroughs

deep-learning

neural-networks

beginner

Discover how deep learning is revolutionizing artificial intelligence and why it’s become the driving force behind recent AI breakthroughs.

Generative Models: Mathematical Foundations and Architectures

machine-learning

generative-models

deep-learning

mathematics

A rigorous mathematical exploration of generative models, including GANs, VAEs, and diffusion models.

Information Theory in Machine Learning: A Practical Guide

machine-learning

information-theory

mathematics

theory

A beginner-friendly guide to information theory in machine learning, with practical examples and intuitive explanations.

Machine Learning: A Beginner’s Guide

machine-learning

fundamentals

python

hands-on

A beginner-friendly guide to machine learning with clear explanations and practical examples.

ML Fundamentals: Understanding the Basics

machine-learning

fundamentals

theory

A comprehensive introduction to machine learning fundamentals, core concepts, and essential terminology that every aspiring ML practitioner should know.

Machine Learning in Everyday Life: A Practical Guide

machine-learning

applications

real-world

beginner

Discover how machine learning is already part of your daily life and how it makes things easier, explained in simple terms with real-world examples.

Machine Learning Theory: Mathematical Foundations Made Simple

machine-learning

theory

mathematics

statistics

A beginner-friendly guide to machine learning theory, with intuitive explanations and practical examples.

Online Learning and Regret Minimization

machine-learning

theory

mathematics

online-learning

A rigorous exploration of online learning theory and regret minimization, covering fundamental algorithms, bounds, and applications.

Optimization Algorithms in Machine Learning: A Deep Dive

machine-learning

optimization

mathematics

algorithms

A comprehensive technical exploration of optimization algorithms in machine learning, covering mathematical foundations and implementation details.

Optimization Theory in Machine Learning

machine-learning

optimization

mathematics

theory

A rigorous exploration of optimization theory in machine learning, covering convex optimization, non-convex optimization, and modern algorithms.

Probabilistic Graphical Models: Mathematical Foundations

machine-learning

probabilistic-models

mathematics

bayesian

A rigorous exploration of probabilistic graphical models, covering mathematical foundations, inference algorithms, and learning methods.

Understanding Reinforcement Learning: A Beginner’s Guide

machine-learning

reinforcement-learning

theory

beginner

A beginner-friendly introduction to reinforcement learning concepts, explained through simple analogies and real-world examples.

Statistical Learning Theory and Concentration Inequalities

machine-learning

theory

mathematics

statistics

A rigorous exploration of statistical learning theory and concentration inequalities, covering fundamental bounds and their applications in machine learning.