How to consistently parse outputs from LLMs using Open AI API and LangChain function calling: evaluating the methods’ advantages and disadvantages — Creating tools with LLMs requires multiple components, such as vector databases, chains, agents, document splitters, and many other new tools. However, one of the most crucial components is the LLM output parsing. If you cannot receive structured responses from your LLM, you will have a hard time working with the…

How to make LangChain chains work with Async calls to LLMs, speeding up the time it takes to run a sequential long chain — In this article, I will cover how to use asynchronous calls to LLMs for long workflows using LangChain. We will go through an example with the full code and compare Sequential execution with the Async calls. Here is the overview of the content. …

Full project walkthrough with the implementation of the DDPG algorithm for the Continuous Control problem of the Reacher environment — Welcome to a fascinating exploration of reinforcement learning in the context of continuous control! In this article, we will dive into a challenging problem: teaching an intelligent agent to control a double-jointed robotic arm in the Reacher environment, a Unity-based simulation developed using the Unity ML-Agents toolkit. …

End to End Project with code of a PyTorch implementation of Deep Reinforcement Learning Agent — Unity is a popular game development engine that allows developers to create games with stunning graphics and immersive gameplay. It is widely used for developing games across various platforms, including mobile, PC, and consoles. However, creating intelligent and challenging game environments is a challenging task for game developers. …

End-to-end phyton project looking at exercise data from an Apple Watch, from data collection to model deployment — In this series of posts, I will go through all steps of an end-to-end machine learning project. From data extraction and preparation to the deployment of the model using an API and finally to the creation of a front end to actually solve the problem helping with decisions. …

Courses review, study tips, and how I did it — So I decided to take the GCP Professional Machine Learning Engineering (PMLE) test but I had only 2 months to do it in order to attain enough certifications to my company be a GCP partner. I knew this was going to be a hard challenge but I jumped at it…

Using a trained model to identify objects on static images and live video — In this article, I’m going to demonstrate how to use a trained model to detect objects in images and videos using two of the best libraries for this kind of problem. …

How to use a state-of-the-art trained NN to solve your image classification problem — The main goal of this article is to demonstrate with code and examples how can you use an already trained CNN (convolutional neural network) to solve your specific problem. Convolutional Networks are great for image problems however, they are computationally expensive if you use a big architecture and don’t have…

Introdução à visão computacional com exemplos práticos — O objetivo desse post é mostrar na prática como conseguimos reconhecer objetos, pessoas e faces em imagens e vídeos ao vivo, usando Python e a biblioteca OpenCV. Requisitos para usar o código: Ter Python instalado; Ter a biblioteca OpenCV instalada; Acesso a um editor de texto. Introdução O que é OpenCV? A Open Source Computer Vision Library (OpenCV) é uma…