Blog | Rajat Patel

📝 Blog Posts

Welcome to my blog! Here I share insights on machine learning, statistics, and my experiences in the tech industry.

🤖 Reinforcement Learning & Deep Learning

Technical Deep Dives

Reinforcement Learning Foundations: From MDPs to Deep Q-Learning - A comprehensive guide to RL fundamentals, covering MDPs, value functions, Q-learning, and Deep Q-Networks with mathematical derivations and code examples

📊 Statistics & Probability

Probability Distributions

Binomial Random Variable and Distribution - Understanding binomial distributions and their applications
Geometric Random Variable and Distribution - Exploring geometric distributions in probability theory
Poisson Distribution - Deep dive into Poisson distributions and real-world examples
Exponential Distribution - Understanding exponential distributions and their properties

Statistical Estimation

Maximum Likelihood Estimation - Fundamentals of MLE and parameter estimation
Maximum Likelihood Estimation for Poisson Distribution - MLE applied to Poisson distributions
Maximum Likelihood Estimation for Exponential Distribution - MLE techniques for exponential distributions

🤖 Machine Learning

Regression Analysis

Linear Regression - Understanding linear regression models and applications
Logistic Regression - Classification using logistic regression techniques

🚀 ML Product Management Series

Navigating the ML Frontier

Part I: Empowering Product and Program Managers in Crafting AI Products - Insights from an engineer’s perspective on building AI products
Part II: Understanding ML Development Life Cycle - Complete guide to ML development processes
Part III: Effective Collaboration with ML Engineers and Data Scientists - Best practices for cross-functional collaboration
Part IV: Communicating and Aligning while Developing ML Products - Strategies for effective communication in ML projects

🔮 What’s Coming Next

I plan to continue writing about:

Advanced RL Techniques - Policy gradients, Actor-Critic methods, and RLHF
Production ML - Scaling ML systems and MLOps best practices
Career Insights - Lessons learned in ML engineering roles
Research Summaries - Key findings from recent ML papers

Stay tuned for more content!

Articles marked with 🔗 are published on Medium. Follow me there for the latest updates!

Reinforcement Learning Foundations: From MDPs to Deep Q-Learning

Introduction Reinforcement Learning (RL) has exploded in popularity — first in game-playing agents, and now in large language models via methods like RLHF (Reinforcement Learning from Human Feedback). These approaches don’t just help models learn context better; they also improve reasoning by teaching them to “think in steps.” My fascination with RL began when the GPT-3 paper was published and ChatGPT emerged as the so-called “tool of the decade.” I wanted to go beyond using these models — I wanted to understand how they work under the hood. That meant building RL concepts from the ground up: deriving equations, implementing toy solutions in environments like CartPole and FrozenLake, and seeing theory come alive in code. ...

📝 Blog Posts#

🤖 Reinforcement Learning & Deep Learning#

Technical Deep Dives#

📊 Statistics & Probability#

Probability Distributions#

Statistical Estimation#

🤖 Machine Learning#

Regression Analysis#

🚀 ML Product Management Series#

Navigating the ML Frontier#

🔮 What’s Coming Next#