Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Gabriel Cassimiro
LLM Output Parsing: Function Calling vs. LangChain
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 generations.
Async calls for Chains with Langchain
So let me set up the problem I had: I have a data frame with a lot of rows and for each of those rows I need to run multiple prompts (chains) to an LLM and return the result to my data frame. When you have multiple rows, let’s say 10K, running 3 prompts for each and each response (if the server is not overloaded) taking about 3–5 seconds you end up waiting for days for the workflow to be completed.
Solving Unity Environment with Deep Reinforcement Learning
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. This is where Deep Reinforcement Learning (DRL) comes into play.
Predicting My Next Workout with Machine Learning: Part 1
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.
Object detection with Tensorflow model and OpenCV
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. For the detection, we need a model capable of predicting multiple classes in an image and returning the location of those objects so that we can place boxes on the image.
Transfer Learning with VGG16 and Keras
This network was trained on the ImageNet dataset, containing more than 14 million high-resolution images belonging to 1000 different labels. If you want to dig deeper into this specific model you can study this paper. Dataset For this demonstration, I will use the tf_flowers dataset. Just as a reminder: The VGG16 network was not trained to classify different kinds of flowers.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Gabriel
Contact Gabriel, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack