Writing AlexNet from Scratch in PyTorch
Learn how to build the AlexNet architecture from scratch using PyTorch. This step-by-step guide covers each layer in detail, helping you understand and implement this classic convolutional neural network.
Object Detection Using Mask R-CNN with TensorFlow 1.14 and Keras
Learn how to perform object detection and instance segmentation using Mask R-CNN with TensorFlow 1.14 and Keras.
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.
Padding In Convolutional Neural Networks
In this article, we explore how and why we use padding in CNNs in computer vision tasks. We’ll then jump into a full coding demo showing the utility of padding.
Advanced Recurrent Neural Networks: Bidirectional RNNs
This series gives an advanced guide to different recurrent neural networks (RNNs). You will gain an understanding of the networks themselves, their architectures, their applications, and how to bring the models to life using Keras.
Pooling In Convolutional Neural Networks
Explore the whys and the hows behind the process of pooling in CNN architectures, and compare 2 common techniques: max and average pooling.
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 […]
A Review of Popular Deep Learning Architectures: AlexNet, VGG16, and GoogleNet
This review explores three foundational deep learning architectures—AlexNet, VGG16, and GoogleNet—that have significantly advanced the field of computer vision.
Checkpointing in TensorFlow
Follow this guide to learn how to directly monitor and checkpoint your models during the training process!
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.
