Real-World Denoising Through Diffusion Model
This overview covers the basic theory behind diffusion modeling, through a breakdown of the “Real-World Denoising via Diffusion Model” paper
Audio Classification with Deep Learning
Conduct auditory classification within a Jupyter Notebook using TensorFlow. Learn about signal processing and techniques for audio classification.
Fine-Tune Mistral-7B with LoRA A Quickstart Guide
Learn how to fine-tune the Mistral-7B model using LoRA for efficient, low-resource training. Step-by-step guide with code, tips, and best practices.
Intro to Optimization in Deep Learning: Vanishing Gradients and Choosing the Right Activation Function
An look into how various activation functions like ReLU, PReLU, RReLU and ELU are used to address the vanishing gradient problem, and how to chose one amongst them for your network.
Faster R-CNN Explained for Object Detection Tasks
Learn how Faster R-CNN works for object detection tasks with its region proposal network and end-to-end architecture.
Introduction to NVIDIA CUDA Achieving Peak Performance with H100 for AI and Deep Learning
This article will help explore the fundamentals of NVIDIA CUDA, its basic architecture, and how developers can use it to optimize GPU performance for a wide range of deep learning tasks.
Deep Learning Architecture Review: DenseNet, ResNeXt, MnasNet & ShuffleNet v2
Understand the strengths and applications of popular deep learning architectures—DenseNet, ResNeXt, MnasNet, and ShuffleNet v2. Learn how these models enhance efficiency, accuracy, and performance in AI and computer vision tasks.
Building Intelligence: Neural Network Basics
Learn how neural networks work with this step-by-step guide. Understand key components, types, and training to build intelligent AI systems from scratch.
Implementation StyleGAN1 from scratch
In this article, we will make a clean, simple, and readable implementation of StyleGAN using PyTorch.
A Guide to the DataLoader Class and Abstractions in PyTorch
We will explore one of the biggest problems in the fields of Machine Learning and Deep Learning: the struggle of loading and handling different types of data.
