Shivang ShrivastavBernoulli vs Binomial: Understanding the Key Differences in Probability DistributionsFrom Coin Tosses to Success Counts: Navigating Two Fundamental DistributionsOct 15Oct 15
Shivang ShrivastavProbability Distributions Made Easy: Improve Your Decision-MakingAn intuitive guide to choosing the right distribution for your data and making better decisions.Oct 14Oct 14
Shivang ShrivastavReinforcement Learning: Model-Based vs Model-Free ApproachesUnderstanding the Fundamentals of Reinforcement Learning and Choosing the Right ApproachOct 14Oct 14
Shivang ShrivastavNavigating Reinforcement Learning: Policy Probability and State Transition ProbabilityUnlocking the Building Blocks of Reinforcement LearningOct 14Oct 14
Shivang ShrivastavP-Value and Null Hypothesis: A Comprehensive GuideWhat is P-Value and the Invisible part of Null HypothesisSep 15Sep 15
Shivang ShrivastavBellman Equation and Value iteration in Dynamic ProgrammingWhat is Bellman Equation. Value iteration explained in detail.Sep 15Sep 15
Shivang ShrivastavAntenna Parameters — Part 2Antenna Field Zones, Radiation Efficiency, Antenna Design Case Study, Directivity, Beam Solid Angle, Gain, Radiated Power, Antenna…Sep 15Sep 15
Shivang ShrivastavAntenna Parameters — Part 1major lobe, minor lobe, HPBW, FNBW, Azimuth, elevation, radiation density, radiated power, beam efficiency, stray factor, radiation patternSep 15Sep 15
Shivang ShrivastavOn Policy Vs Off Policy in Monte Carlo Method in Reinforcement LearningDifference between on-policy and off-policy for MCMSep 8Sep 8
Shivang ShrivastavMarkov Decision Processes: A Guide to Dynamic & Monte Carlo MethodsExploring the Relationships between MDPs, Dynamic Programming, and Monte Carlo SimulationsSep 8Sep 8