LeNet-5 from Scratch with PyTorch A Beginner’s Guide
A complete walkthrough to build LeNet-5 from scratch using PyTorch. Perfect for beginners exploring deep learning and CNNs.
PyTorch Loss Functions
Follow this guide to learn about the various loss functions available to use with PyTorch.
Writing VGG from Scratch in PyTorch
We explore writing VGG from Scratch in PyTorch. Learn how to create, train, and evaluate a VGG neural network for CIFAR-100 image classification.
Understanding Filters in Convolutional Neural Networks (CNNs)
Explore techniques for filtering image data and learn what these filters do to an image as it passes through the layers of a Convolutional Neural Network
Image Compression Using Autoencoders in Keras
In this tutorial we cover a thorough introduction to autoencoders and how to use them for image compression in Keras.
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.
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.
Deep Learning Architectures Explained: ResNet, InceptionV3, SqueezeNet
Understand the basics of ResNet, InceptionV3, and SqueezeNet architecture and how they power deep learning models. Learn their architectures, key features, and how they improve accuracy and efficiency.
Capsule Networks: A Quick Primer
Introduction Whether you're new to deep learning or a serious researcher, you've surely encountered the term convolutional neural networks (CNNs). They are one of the most researched and top-performing architectures in the field. That being said, CNNs have a few drawbacks in recognizing features of input data when they are in different orientations. To address […]
