Since its publication in a 2017 paper by OpenAI, Proximal Policy Optimization (PPO) is widely regarded as one of the state-of-the-art algorithms in Reinforcement Learning. Indeed, PPO has…
Dive into LSTMs, understand their mathematics, implement them from scratch, and explore their applications in algorithmic trading.
This article discusses the newly released Phi3 large language model from Microsoft, an LLM capable of performing various tasks with a uniquely large context window relative to the model’s size. I…
YOLO has long been one of the first go-to models for object detection tasks. It’s fast and accurate. Besides, the API is concise and easy to work on. The lines of code required to run a training or…
As query complexity increases, there is a growing need to keep the code organized, modular, and readable. Therefore, it is important to know techniques to maintain code organization. This skill will…
Enhancing Direct Answer Accuracy in RAG Setup with Self-Retrieval Mechanisms. Leveraging the power of LLMs to significantly enhance the quality of document context retrieved for direct answer generation in your RAG….
Our focus this week was on real-world testing and fine-tuning of LLama-3 and Phi-3, as well as some exciting progress in medical AI. We have been monitoring resounding positive feedback on LLama-3…
Business stakeholders are starting to recognise the value machine learning models bring to their operations, gaining a deeper understanding of their benefits and drawbacks. Simultaneously, there’s a…
The transformer neural architecture is state-of-the-art. It scales very well, i.e., the larger models learn better, and is efficient to train thanks to the parallel computation of the attention.
In the first part of this series, we've discussed the basic ideas of computer simulations and how you can leverage them to answer "what-if" questions. It's impossible to talk about simulations…
