In Reinforcement Learning, we either use Monte Carlo (MC) estimates or Temporal Difference (TD) learning to establish the ‘target’ return from sample episodes. Both approaches allow us to learn from…
This week in AI, we highlight further evidence of the adoption of Large Language Models (LLMs) driving significant revenue growth in the Western market, as well as the introduction of new AI models…
Meta’s latest release, Llama 2, is gaining popularity and is incredibly interesting for various use cases. It offers pre-trained and fine-tuned Llama 2 language models in different sizes, from 7B to…
Data-driven storytelling is a way of communicating numerical information through narration and visualization. Its goal is to engage the audience and help them better understand the main conclusions…
As interest in sustainability is growing, environmental data science comes into focus. We show examples, outline challenges and perspectives.
In the previous article — Part 2 — we discovered a few solution algorithms to solve the Markov Decision Process (MDP), namely the Dynamic Programming method and the Monte Carlo method. The Dynamic…
RAG (Retrieval-Augmented Generation) is an AI framework that enhances the quality of Language Model-generated responses by incorporating external knowledge sources. It bridges the gap between…
Compilers are seeing a renaissance in the era of generative AI. In the context of AI, a compiler is responsible for translating a neural network architecture into executable code in a specific…
This article introduces the centering and scaling concepts. With a real-world use case, I explain the advantages of the center and scale the data. Technically, we compare the MinMaxScaler…
Isn’t it amazing that everything you need to excel in a perfect information game is there for everyone to see in the rules of the game? Unfortunately, for mere mortals like me, reading the rules of a…
