InTowards AIbyTowards AI Editorial TeamHow did Binary Cross-Entropy Loss Come into Existence?Exploring the Genesis of Binary Cross-Entropy Loss FunctionMar 7, 20231Mar 7, 20231
InTowards AIbyLeo WangUNet++ Clearly Explained — A Better Image Segmentation ArchitectureIn this article, we are going to introduce you to UNet++, essentially an upgraded version of UNet. This article is designed to help you…Jul 22, 20221Jul 22, 20221
InTDS ArchivebyNeeraj KrishnaUnderstanding and Implementing Faster R-CNN: A Step-By-Step GuideDemystifying Object DetectionNov 2, 202213Nov 2, 202213
InAnalytics VidhyabyHrithick SenUnderstanding GANs — Deriving the Adversarial loss from scratchGenerative adversarial networks or GANs for short are an unsupervised learning task where the generator model learns to discover patterns…Mar 3, 20212Mar 3, 20212
InTDS ArchivebyLakshmi AjayDecoding the basic Math in GAN — Simplified VersionGANs have been a revolution in deep learning over the last decade. Since the first version of GAN that was released in 2014 by Ian…Feb 24, 20221Feb 24, 20221
InTDS ArchivebyChiara CampagnolaVisualizing regularization and the L1 and L2 normsWhy does minimizing the norms induce regularization?Oct 23, 20201Oct 23, 20201
InTDS ArchivebyEkin TiuUnderstanding Latent Space in Machine LearningLearn a fundamental, yet often ‘hidden,’ concept of deep learningFeb 4, 202032Feb 4, 202032
InTDS ArchivebyMinh TranUnderstanding U-NetU-Net has become the go-to method for image segmentation. But how did it came to be?Nov 15, 20221Nov 15, 20221
InTDS ArchivebyOrhan G. YalçınImage Generation in 10 Minutes with Generative Adversarial NetworksUsing Unsupervised Deep Learning to Generate Handwritten Digits with Deep Convolutional GANs using TensorFlow and the MNIST DatasetSep 18, 20204Sep 18, 20204
KaushikladeImage Generation using Generative Adversarial Networks (GANs)Understanding the Generative Adversarial NetworkMar 24, 20211Mar 24, 20211
InTDS ArchivebyRichmond AlakeDeep Learning: Understand The Inception ModuleThe Deep Learning Architecture Inspired By An Internet Meme — and its technical information and detailsDec 22, 20203Dec 22, 20203
Siddhesh BangarResnet Architecture ExplainedIn their 2015 publication “Deep Residual Learning for Image Recognition,” Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun created a…Jul 5, 20221Jul 5, 20221
InAIGuysbyVishal RajputYolov7: Making YOLO Great AgainYOLO V7 | yolov7 |🚀Object detection State-of-the-art in town🔥Jul 19, 20221Jul 19, 20221
InAnalytics VidhyabyJamie DowatInternal Covariate Shift: An Overview of How to Speed up Neural Network TrainingWhen beginning to build your first neural network, the process seems akin to feeling around for a needle in a haystack, blindfolded. There…Mar 29, 20212Mar 29, 20212
InTDS ArchivebyAlessandro PaticchioIntroduction to Reinforcement Learning: Temporal Difference, SARSA, Q-LearningReinforcement Learning is one of the most intricate fields of Machine Learning, due to its mathematical complexity, as well as the…Oct 11, 2022Oct 11, 2022
InTDS ArchivebyEligijus BujokasEfficient memory management when training a deep learning model in PythonHow to use big data on a small computer using Tensorflow, Python and iteratorsOct 11, 2022Oct 11, 2022
InTDS ArchivebyHari DevanathanThe Basics of Object Detection: YOLO, SSD, R-CNNOverview of how object detection works, and where to get startedOct 11, 20221Oct 11, 20221
InTDS ArchivebyRaz RotenbergHow to Break GPU Memory Boundaries Even with Large Batch SizesOvercoming the problem of batch size and available GPU memory in training neural networksJan 19, 2020Jan 19, 2020
InTDS ArchivebyDaniel HuynhImplementing a batch size finder in Fastai : how to get a 4x speedup with better generalization !I will share with you my thoughts on batch size, show why a big one might help, and show how to find a good size using a paper from OpenAINov 4, 20191Nov 4, 20191
InTDS ArchivebyRenu KhandelwalConvolutional Neural Network: Feature Map and Filter VisualizationLearn how Convolutional Neural Networks understand images.May 18, 20206May 18, 20206
