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comprehensive guide to Quantization in Machine Learning Systems: Optimizing Performance for Edge Devices 2024 As machine learning (ML) continues to evolve, deploying complex models on resource-constrained devices such as mobile phones, embedded systems, and IoT devices has become a critical challenge. In these environments, computational resources like processing power, memory, and energy are limited. This […]

The Rise of Tiny Machine Learning: Empowering Edge Devices 2024 Machine learning (ML) has significantly impacted various industries by enabling machines to learn from data and make intelligent decisions. Traditionally, ML models required heavy computational power and were processed in cloud data centers. However, with the advent of edge computing, tiny machine learning (TinyML) is

Optimizing Machine Learning Models for Edge Devices: A comprehensive Deep Dive 2024 In the era of IoT and edge computing, machine learning (ML) models are increasingly being deployed on edge devices to provide faster, real-time decision-making. The benefits of deploying AI at the edge are immense, from improving response times to ensuring privacy and reducing

Temporal Difference Learning: Bridging Monte Carlo and Dynamic Programming in Reinforcement Learning 2024 Introduction In Reinforcement Learning (RL), an agent learns to make decisions by interacting with an environment. Temporal Difference (TD) Learning is a key algorithm that lies between Monte Carlo (MC) methods and Dynamic Programming (DP) in the RL spectrum. Unlike MC, which

Monte Carlo Methods in Reinforcement Learning: A Comprehensive Guide 2024 Introduction In Reinforcement Learning (RL), agents interact with an environment by taking actions and receiving rewards to learn an optimal policy. Monte Carlo (MC) methods provide a way to estimate value functions by using random sampling and episodic learning without requiring prior knowledge of environment

comprehensive guide to Markov Decision Processes (MDP) and Dynamic Programming in Reinforcement Learning 2024 Introduction In Reinforcement Learning (RL), an agent interacts with an environment by taking actions and receiving rewards to maximize long-term returns. Markov Decision Processes (MDP) provide a mathematical framework to model decision-making problems where outcomes are partly random but also controlled

Multi-Armed Bandits: A Fundamental Problem in Reinforcement Learning 2024 Introduction The Multi-Armed Bandit (MAB) problem is a foundational challenge in Reinforcement Learning (RL) that models decision-making under uncertainty. The problem is inspired by a slot machine scenario where a gambler must decide which arm to pull to maximize their winnings. This simple yet powerful framework

comprehensive guide to Imitation Learning: Teaching AI by Demonstration 2024 Introduction Imitation Learning (IL) is a machine learning approach where an AI agent learns by observing expert demonstrations rather than through trial-and-error reinforcement learning. This method is widely used in robotics, self-driving cars, gaming AI, and healthcare, where training an agent from scratch is inefficient

comprehensive guide to MuZero: Mastering Games Without Knowing the Rules 2024 Introduction In 2020, DeepMind introduced MuZero, a groundbreaking AI that surpassed AlphaZero by learning and mastering complex games like Atari, Go, Chess, and Shogi—without knowing their rules. Unlike its predecessors, MuZero does not require a predefined environment model; instead, it learns everything from scratch

Mastering the Game of Go Without Human Knowledge: The AlphaGo Zero Breakthrough 2024 Introduction In 2017, DeepMind introduced AlphaGo Zero, a self-learning AI that surpassed all previous Go-playing programs, including its predecessor AlphaGo, by learning from scratch—without human input. Unlike earlier versions, AlphaGo Zero was trained purely through self-play reinforcement learning, achieving superhuman intelligence without