LLaMA 2: a model overview and demo tutorial in Jupyter Notebooks
This tutorial shows how the LLaMA 2 model has improved upon the previous version, and details how to run it freely in a Jupyter Notebook.
Overview of the cloud provider Gradient Platform Guardrails
Explore the cloud provider’s Gradient Platform guardrails to ensure secure, ethical, and efficient use of generative AI tools for developers.
CODEGEN: A Transformative Open-Source Language Model for Versatile Program Synthesis
In this tutorial, we discuss CODEGEN, one of the hottest Language Modeling projects tackling code generation.
Intro to optimization in deep learning: Gradient Descent
An in-depth explanation of Gradient Descent and how to avoid the problems of local minima and saddle points.
Qwen3-Next-80B-A3B-Instruct: Efficient Long-Context AI
Explore Qwen3-Next-80B-A3B-Instruct, a next-generation large language model optimized for efficiency, scalability, and long-context understanding.
Como enganar uma rede neural no Python 3
O autor selecionou a Dev Color para receber uma doação como parte do programa Write for DOnations. Será que uma rede neural para classificação de animais pode ser enganada? Enganar um classificador de animais…
How To Trick a Neural Network in Python 3
In this tutorial, you will try “fooling” or tricking an animal classifier. As you work through the tutorial, you’ll use OpenCV, a computer-vision library, and PyTorch, a deep learning library. By the end of the tutorial, you will have a tool for tricking neural networks and an understanding of how to defend against tricks.
How to Build a Photogrammetry Pipeline on a GPU Droplet
Learn how to set up a powerful photogrammetry pipeline using GPU cloud servers. This step-by-step guide covers installation, configuration, and optimization for fast 3D model creation from images.
Sliding Window Attention: Efficient Long-Context Modeling
‘Learn how sliding window attention enables efficient long-context modeling in modern AI systems. Understand its benefits and use cases in LLMs.’
LLM Inference Optimization 101
‘The goal of this article is to give readers an overview of current ways in which researchers and deep learning practitioners are optimizing LLM inference.’