#### Transformers : [You just need Attention]

Natural language processing or NLP is a subset of machine learning that deals with text analytics. It is concerned with the interaction of human language and computers. There have been…

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#### Transformers : [You just need Attention]

#### Reinforcement Learning: How Optimistic Initial Values effect Exploration [Part 8]

#### Reinforcement Learning: Action-Value Methods wrt Incremental Implementation[Part 6]

#### Reinforcement Learning: The N-Armed Bandit Problem- Action-Value Methods [Part 5]

#### Reinforcement Learning: The N-Armed Bandit Problem- Overview [Part 4]

#### Elements of Reinforcement Learning [Part 3]

#### What do you mean by stochastic in Mathematics?

#### Real World Examples of Reinforcement Learning [Part 2]

#### Reinforcement Learning – Introduction [Part 1]

#### Next Basket Recommendation: Temporal-Item-Frequency-Based User-K Nearest Neighbors (TIFU-KNN) Model

#### Course on Advanced Prompt engineering techniques ! [How to get better response from ChatGPT]

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Natural language processing or NLP is a subset of machine learning that deals with text analytics. It is concerned with the interaction of human language and computers. There have been…

Multi-Armed Bandit Problem The multi-armed bandit problem is a classic example in reinforcement learning and decision theory. It is framed around the idea of a gambler facing multiple slot machines…

Action-Value Method" with respect to Incremental Implementation The term "Action-Value Method" with respect to Incremental Implementation refers to a strategy used in reinforcement learning to estimate the value of taking…

Part 1: Action-Value Methods The term "action-value method" refers to a set of techniques used in reinforcement learning to estimate the expected reward (or value) associated with taking a particular…

N-Armed Bandit Problem In the context , the term "bandit" refers to the multi-armed bandit problem in reinforcement learning. What is a Multi-Armed Bandit? The multi-armed bandit is a classic…

Key Sub-elements: Beyond the agent and the environment, four main sub-elements define a reinforcement learning system: Policy Reward Signal Value Function Model of the Environment (optional) 1. Policy: Definition: A…

"Stochastic" refers to systems, processes, or phenomena that involve randomness or unpredictability. In a stochastic system, the outcome is not deterministic, meaning that even if you start from the same…

Chess Example: A master chess player makes a move informed by: Planning possible replies and counter-replies. Making immediate, intuitive judgments about the desirability of specific positions and moves. Adaptive Controller…

Reinforcement Learning (RL) Problem Overview: The fundamental idea is to capture key aspects of the real-world problem where a learning agent interacts with its environment to achieve a goal. Key…

Temporal-Item-Frequency-Based User-KNN model, which is a neighbor-based approach designed for Next-Basket Recommendation (NBR) TIFU-KNN Model Overview Purpose and Approach: Purpose: TIFU-KNN is used for predicting the next set of items…

In today's AI-driven world, mastering the art of prompt engineering is crucial for optimizing interactions with large language models (LLMs) like GPT-4 and others. This Prompt engineering course deals with…