Datasets:

nepalprabin
/

blog_dataset

source stringlengths 65 120	full_text stringlengths 2.6k 14.4k	title stringlengths 22 97
https://nepalprabin.github.io./posts/2020-06-05-paper-explanation-going-deeper-with-convolutions-googlenet.html	Paper Explanation: Going deeper with Convolutions (GoogLeNet) Google proposed a deep Convolution Neural Network named inception that achieved top results for classification and detection in ILSVRC 2014. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image ...	Paper Explanation: Going deeper with Convolutions (GoogLeNet)
https://nepalprabin.github.io./posts/2021-07-27-illustrated-vision-transformers.html	Illustrated Vision Transformers Introduction Ever since Transformer was introduced in 2017, there has been a huge success in the field of Natural Language Processing (NLP). Almost all NLP tasks use Transformers and it’s been a huge success. The main reason for the effectiveness of the Transformer was its ability to h...	Illustrated Vision Transformers
https://nepalprabin.github.io./posts/2023-05-15-gpt4-summary.html	Brief overview of GPT-4 Since the release of ChatGPT, there has been significant interest and discussion within the broader AI and natural language processing communities regarding its capabilities. In addition to this, ChatGPT has captured the attention of the internet at large due to its remarkable ability to genera...	Brief overview of GPT-4
https://nepalprabin.github.io./posts/2020-08-23-neural-style-transfer-and-its-working.html	Neural style transfer and its working Have you ever used an app called Prisma that styles your image using popular paintings and turns your photo stunning? If that’s the case then, the app you are using is the result of style transfer; a computer vision technique that combines your images with artistic style. Introdu...	Neural style transfer and its working
https://nepalprabin.github.io./posts/2020-08-04-general-adversarial-networks-gans.html	General Adversarial Networks (GANs) “General Adversarial Netsis the most interesting idea in the last 10 years in machine learning”. This was the statement from Yann LeCun regarding GANs when Ian Goodfellow and co-authors introduced it in 2014. After its first introduction, many research papers are published with vari...	General Adversarial Networks (GANs)
https://nepalprabin.github.io./posts/2025-03-02-huggingface-smolagents-solutions.html	Huggingface AI Agents Quiz Solutions I have been diving into AI agents through Huggingface’s AI Agents Course. This course offers a comprehensive understanding of how to build and deploy AI agents using thesmolagentslibrary. In this blog, I’ll share insights from the course (Unit 2) and provide code snippets to illust...	Huggingface AI Agents Quiz Solutions
https://nepalprabin.github.io./posts/2021-10-25-autocorrect-and-minimum-edit-distance.html	Autocorrect and Minimum Edit Distance This is my brief note fromDeepLearning.AI’sNLP Specialization Course. What is Autocorrect? Autocorrect is an application that changes misspelled word into a correct word. When writing messages or drafting an email, you may have noticied that if we type any words that is misspell...	Autocorrect and Minimum Edit Distance
https://nepalprabin.github.io./posts/2022-10-19-text-summarization-nlp.html	Text Summarization NLP Text summarization is one of the Natural Language Processing (NLP) tasks where documents/texts are shortened automatically while holding the same semantic meaning. Summarization process generates short, fluent and accurate summary of the long documents. The main idea of text summarization is to ...	Text Summarization NLP
https://nepalprabin.github.io./posts/2021-01-01-deep-residual-learning-for-image-recognition-resnet-paper-explained.html	Deep Residual Learning for Image Recognition (ResNet paper explained) Deep Neural Networks tend to provide more accuracy as the number of layers increases. But, as we go more deeper in the network, the accuracy of the network decreases instead of increasing. As more layers are stacked, there occurs a problem ofvanishi...	Deep Residual Learning for Image Recognition (ResNet paper explained)
https://nepalprabin.github.io./posts/2020-09-21-mobilenet-architecture-explained.html	MobileNet Architecture Explained In this blog post, I will try to write about the MobileNets and its architecture. MobileNet uses depthwise separable convolutions instead of standard convolution to reduce model size and computation. Hence, it can be used to build light weight deep neural networks for mobile and embedd...	MobileNet Architecture Explained
https://nepalprabin.github.io./posts/2020-05-09-vggnet-architecture-explained.html	VGGNet Architecture Explained VGGNet is a Convolutional Neural Network architecture proposed by Karen Simonyan and Andrew Zisserman of University of Oxford in 2014. This paper mailny focuses in the effect of the convolutional neural network depth on its accuracy. You can find the original paper of VGGNet which is titl...	VGGNet Architecture Explained
https://nepalprabin.github.io./posts/2020-04-24-alexnet-architecture-explained.html	AlexNet Architecture Explained AlexNet famously won the 2012 ImageNet LSVRC-2012 competition by a large margin (15.3% vs 26.2%(second place) error rates). Here is the link to originalpaper. Major highlights of the paper - Used ReLU instead of tanh to add non-linearity. - Used dropout instead of regularization to dea...	AlexNet Architecture Explained
https://nepalprabin.github.io./posts/2021-03-26-simclr-explained.html	Paper Explanation: A Simple Framework for Contrastive Learning of Visual Representations (simCLR) Various self-supervised learning methods have been proposed in recent years for learning image representations. Though a lot of methods have been proposed, the performance of those methods was found less effective in term...	Paper Explanation: A Simple Framework for Contrastive Learning of Visual Representations (simCLR)
https://nepalprabin.github.io./posts/2023-07-04-augmented-language-models.html	Augmenting Large Language Models: Expanding Context and Enhancing Relevance With the rise of ChatGPT and other large language models (LLMs), the potential for AI to surpass human capabilities has become a topic of both fascination and concern. While LLMs excel at understanding language, following instructions, and rea...	Augmenting Large Language Models: Expanding Context and Enhancing Relevance
https://nepalprabin.github.io./posts/2020-12-08-self-supervised-learning.html	Self-supervised Learning I have been exploring self-supervised learning and been through papers and blogs to understand it. Self-supervised learning is considered the next big thing in deep learning and why not! If there is a way to learn without providing labels, then this enables us to leverage a large amount of unl...	Self-supervised Learning
https://nepalprabin.github.io./posts/2020-08-15-deep-convolutional-general-adversarial-networks-dcgans.html	Deep Convolutional Generative Adversarial Networks (DCGANs) DCGAN (Deep Convolutional General Adversarial Networks) uses convolutional layers in its design. Architectural Details for DCGAN - Comprised convolutional network without max-pooling. Instead, it uses convolutional stride and transpose convolution for downs...	Deep Convolutional Generative Adversarial Networks (DCGANs)